{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Understanding Train Test Split using Scikit-Learn (Python)</h1> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>What is the train test split procedure</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Train Test Split to Tune Models using Python</h2>\n",
    "\n",
    "\n",
    "![]()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Consequences of NOT using Train Test Split\n",
    "</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Train Test Split to Tune Models using Python </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Import Libraries</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Load the Dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>221900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>538000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>604000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>510000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living  sqft_lot  floors     price\n",
       "0         3       1.00         1180      5650     1.0  221900.0\n",
       "1         3       2.25         2570      7242     2.0  538000.0\n",
       "2         2       1.00          770     10000     1.0  180000.0\n",
       "3         4       3.00         1960      5000     1.0  604000.0\n",
       "4         3       2.00         1680      8080     1.0  510000.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/mGalarnyk/Tutorial_Data/master/King_County/kingCountyHouseData.csv'\n",
    "df = pd.read_csv(url)\n",
    "# Selecting columns I am interested in\n",
    "columns = ['bedrooms','bathrooms','sqft_living','sqft_lot','floors','price']\n",
    "df = df.loc[:, columns]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Arrange Data into Features and Target </h3>\n",
    "\n",
    "![]()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['bedrooms','bathrooms','sqft_living','sqft_lot','floors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.loc[:, ['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Split Data into Training and Testing Sets (train test split)</h3>\n",
    "\n",
    "![](images/trainTestSplitBoston.png)\n",
    "\n",
    "The colors in the image indicate which variable (X_train, X_test, y_train, y_test) the data from the dataframe df went to for this particular train test split. \n",
    "\n",
    "In the code below, `train_test_split` splits the data and returns a list which contains four NumPy arrays. `train_size = .75` puts 75% of the data into a training set and the remaining 25% into a testing set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size = .75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21613, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows and columns in features matrix before split\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21613, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows and columns in target before split\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16209, 5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5404, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16209, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5404, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice in the code above that roughly 75 percent of the rows went to the training set (16209/ 21613 = .75) and 25 percent went to the test set (5404 / 21613 = .25). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Understanding the random_state Parameter</h3>\n",
    "\n",
    "![](images/changingRandomState.png)\n",
    "\n",
    "The image above shows that if you select a different value for random state, different information would go to X_train, X_test, y_train, and y_test. \n",
    "\n",
    "The random_state is a pseudo-random number parameter that allows you to reproduce the same results every time you run them. It is useful for testing that your model was made correctly since it provides you with the same train test split each time. It is also useful for tutorials and talks so that you get the exact same results as the person giving the tutorial. \n",
    "\n",
    "However, it is recommended you remove it if you are trying to see how well it generalizes to new data. If you are curious how the image was made above, I recommend you download and run the KingCountySplit notebook as pandas styling functionality doesn't always render on GitHub."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Scikit-learn Modeling Pattern </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1</b>: Import the model you want to use.\n",
    "\n",
    "In scikit-learn, all machine learning models are implemented as Python classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2</b>: Make an instance of the Model\n",
    "\n",
    "In the code below, I set the max_depth = 2 to preprune my tree to make sure it doesn't have a depth greater than 2. I should note the next section of the tutorial will go over how to choose an optimal max_depth for your tree.\n",
    "Also note that in my code below, I made random_state = 0 so that you can get the same results as me."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = DecisionTreeRegressor(max_depth = 2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3</b>: Train the model on the data, storing the information learned from the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to see another example of train_test_split being used in a machine learning context, you can check out my Understanding Decision Trees for Classification post."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4</b>: Predict labels of unseen (test) data\n",
    "\n",
    "For DecisionTreeRegressor, predictions are the mean target (price in this case) of each leaf node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 406622.58288211, 1095030.54807692,  406622.58288211,\n",
       "        406622.58288211,  657115.94280443,  406622.58288211,\n",
       "        406622.58288211,  657115.94280443,  657115.94280443,\n",
       "       1095030.54807692])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can predict for multiple observations\n",
    "reg.predict(X_test[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17384</th>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1430</td>\n",
       "      <td>1650</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bedrooms  bathrooms  sqft_living  sqft_lot  floors\n",
       "17384         2        1.5         1430      1650     3.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also predict for 1 observation. \n",
    "\n",
    "reshape is used to make sure we have two dimensional data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00e+00, 1.50e+00, 1.43e+03, 1.65e+03, 3.00e+00])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.iloc[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00e+00, 1.50e+00, 1.43e+03, 1.65e+03, 3.00e+00])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = X_test.iloc[0]\n",
    "test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([406622.58288211])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(X_test.iloc[0].values.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image below sheds some light on how the trained decision tree comes to a prediction for the 1 observation predicted for in the code above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/housePricePredictionExample.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are curious how these sort of diagrams are made, consider checking out my tutorial [Visualizing Decision Trees using Graphviz and Matplotlib](https://towardsdatascience.com/visualizing-decision-trees-with-python-scikit-learn-graphviz-matplotlib-1c50b4aa68dc). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>NOT IN BLOG: Visualize Decision Tree using Matplotlib</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16209, 5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16209"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10527 + 4336 + 1144 + 202"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n",
    "tree.plot_tree(reg,\n",
    "              feature_names = features,\n",
    "              filled = True);\n",
    "\n",
    "#fig.savefig('images/regressiontree.png', dpi =300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>NOT IN BLOG: Visualize Decision Tree using Graphviz</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntree.export_graphviz(reg,\\n                     out_file=\"images/temp.dot\",\\n                     feature_names = features,\\n                     filled = True)\\n'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "tree.export_graphviz(reg,\n",
    "                     out_file=\"images/temp.dot\",\n",
    "                     feature_names = features,\n",
    "                     filled = True)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not going to run unless you have graphviz installed and added to your path\n",
    "#!dot -Tpng -Gdpi=300 images/temp.dot -o images/temp.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Measuring Model Performance</h3>\n",
    "\n",
    "While there are other ways of measuring model performance (root-mean-square error, mean absolute error, mean absolute error, etc), we are going to keep this simple and use R^2 otherwise known as the coefficient of determination as our metric. \n",
    "R2 is defined as:\n",
    "\n",
    "INSERT EQUATION HERE. \n",
    "\n",
    "\n",
    "If you want to learn more about different metrics, [Vipul Gandhi](https://www.kaggle.com/vipulgandhi) has a informative and long Kaggle Kernel on it [here](https://www.kaggle.com/vipulgandhi/how-to-choose-right-metric-for-evaluating-ml-model).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4380405655348807\n"
     ]
    }
   ],
   "source": [
    "# The score method returns the accuracy of the model\n",
    "# Show train and test score of model as important for what we are doing. \n",
    "\n",
    "score = reg.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might be wondering if our R^2 is good for our model. In general the higher the R^2, the better the model fits the data. It also depends on your field of study. Something harder to predict will in general have a lower R^2. My argument below is that for housing data, we should have a higher R^2 based on our data. \n",
    "\n",
    "Here is why. Domain experts generally agree that one of the most important factors in housing prices is location. After all, if you are looking for a home, most likely you care where it is located. As you can see in the tree below, the decision tree only incorporates sqft_living\n",
    "\n",
    "![](images/treeNoCustomarrows.png)\n",
    "\n",
    "Even if the model was performing very well, it is unlikely that our model would get buy in from stakeholders or coworkers as traditionally speaking, there is more to homes than sqft_living. \n",
    "\n",
    "Note that the original dataset has location information like 'lat' and 'long'. The image below visualizes the prices of all the houses in the dataset based on  'lat' and 'long'. There seems to be a clear trend in the data. \n",
    "\n",
    "The trick for you is to design a model that picks up on location as it is likely places like Zillow found a way to incorporate that into their models.\n",
    "\n",
    "![](images/HousePriceLatLong.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Tuning the Depth of a Tree</h3>\n",
    "There are a lot of different ways to hyperparameter tune a decision tree for regression. One way is to tune the max_depth hyperparameter. The code below outputs the accuracy for decision trees with different values for max_depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depth_range = list(range(1, 25))\n",
    "# List to store the average RMSE for each value of max_depth:\n",
    "r2_list = []\n",
    "for depth in max_depth_range:\n",
    "    reg = DecisionTreeRegressor(max_depth = depth,\n",
    "                            random_state = 0)\n",
    "    reg.fit(X_train, y_train)   \n",
    "    \n",
    "    score = reg.score(X_test, y_test)\n",
    "    r2_list.append(score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph below shows that the best model R² is when the hyperparameter max_depth is equal to 5. This process of selecting the best model (max_depth = 5 in this case) among many other candidate models (with different max_depth values in this case) is called model selection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VK1fGmTNnZJvsvHV1b9++bfD6hIQELF26FL/++iv+/fdf/Ru6/K5cuWLwvp6enrJvpKpWrWrwcffv3w8A6Nq1q8H7GZKXyytXruCJJ56QvV3e8yH3fD/KXM9hs2bNDF7u4OCAypUr4/r167I//5LIzs5GQkICAKlhnDBhgsHb5X3IMP/P5YUXXoCnpyeuXr2Kli1bIiQkBB07doSvr6/Z63xU+fLlERMTg48++gjffPMN9u7di99//x0XLlxAbm4udu/ejd27d2PMmDGYN2+e/n6HDh3S/5t+4YUXZI+f96FUU3NAZAgbZCILeffdd7FhwwasXr0aPXv2xMaNG+Hg4FBgRQY5SUlJAB42GYY89dRTAKRRsOTkZJQtWxa3bt3S/2dobCk0uePmjQinpKQgJSXlsXWae+Qp/yYIZcqUgbu7O+rXrw9/f3+8/fbbsLe3L1An8PBnVZw6S7IjXv7HNeV5kqvTlBqqVKkiO0pepkwZAJB9o5J3vaEVHubOnYvx48frV5soU6YMKlSoAEdHRwBSw5menm6waQaAcuXKydbs7Oxs8HGvX78O4OGa4abIe74zMzP19zfG1Fya6zkszs/BHG7duqVvGB9ducGQ/KuZVKhQAatXr0bfvn3x559/4r333gMAPPHEE+jUqRMGDhwo+ybbXKpXr47Q0FD9pkoXL17Exo0bMWPGDNy6dQuffvopXn75Zf1vI/L/uzdnDogM4RQLIgt59dVXUblyZfz8889YtGgR7t27hy5duhic9iDH1CXZiiqvIXpUbm4uAGDhwoUQ0mcUjP5p27atWevKvwnC5cuXcfLkSWzcuBGBgYH65jh/nYDUxD2uzvPnzxt8vLzmsaRK8jyZq4aiOnHiBCZMmAAhBEaMGIETJ04gIyMDt27d0j8Heb+qlstLcRTnWHnPt7+/v0m5jIiIKPJjWOrfmiXl/3fwxx9/mPSzya9r1644f/48oqOj0atXL3h7e+PatWv44osv0LZt21LfmKd69eoICwvDb7/9pp/u8vnnn+uvzzvfChUqmHSupk61ITKEDTKRhdjb2+Ptt99Gbm4uJk+eDODhGsmPkzeqeOHCBdnb5E0x0Ol0+qbb09NT33DJ/VocgOzc4bxfx588edKkOpWSfztepWrNP/JryvNUktFqS9i4cSNyc3PRuXNnREVF4ZlnninUrJsySldUeVMkjP3MHmWpXFr7c1ixYkX9c1bcn427uzuGDBmC9evX4/Llyzhx4gSGDBkCAFi+fDm2bNlitnpNVadOHbz00ksAgDNnzugvzz9d6HHrwBOVFBtkIgvKm2+clZWFChUqoFu3bibdr0mTJgCkD/bJjbj9/PPPAKT/TMqWLQtA+oBK3u598fHxBu8nhJC9Lm8+7ObNmy3yK2Fz8fX11f9n+c033yhSQ82aNfUbesTFxRm8Td58SuDhc6oWeU3f888/b/D6Bw8e6OcLm1Pelt3G5rg/Ki+Xf/31l36OvTlY+3Po4OCg32zDXP8OnnnmGURHR+ufp7zNNfLkTfUx528VDMn/mpbHz89P/5uk4pxv3mcVLF07aQMbZCILatq0KSIiIjB27FgsWLBA9lPmj8r71faJEyfw/fffF7r++vXrWLp0KQBpxYz8evbsCUAa/TE0L3HdunWyUw769esHOzs7XLlyBbNmzTJaoyU+dFQUeTt3LV682OhOYUII/QexzEmn0yEgIACANCXF0HzHFStW4PLly9DpdPrnVC3c3d0BPNzZ7FEff/wx7t+/b/bHDQoKgk6nw+nTp7Fs2TKT7vPKK6/o5yyHhYXJ7mwHFC2X1v4cAg//HWzcuFG2yc+T/2eTN3dZTt4udo9OPclb/UJul8XHOX78+GNHf69fv64fAGjcuLH+8nLlyuGtt94CAMyYMcPobziys7MLfY6ipLWTbWGDTGRhU6dOxdy5c01avSJP69at9cu7DRw4EF9//bW+Kfj999/RqVMn3L59G1WqVMHo0aML3DckJASVK1dGcnIyOnfujD///BOANIodGxuLIUOG6JujR9WvX1//gZmpU6ciJCQE//zzj/76lJQU7Ny5E0FBQfpGXCkTJ05EzZo18eDBA7Rp0wYxMTEF/kO8dOkSli9fjqZNm+Lbb7+1SA2TJk1C2bJlceXKFbz22mv466+/AEhNxfLlyzFq1CgAwKBBgwwuA6akjh07AgC2bNmCmTNn6pvDpKQkjBs3DrNmzULFihXN/rgNGjTQfyAsJCQEERERuHHjBgBptYXExERERETo3wAC0khpVFQUdDoddu7ciU6dOuHAgQP6kcDs7Gz8/vvv+kwUhTU/h4BUV4sWLZCbm4vXX38dCxcuLPDG+MaNG1i7di3atm2LhQsX6i9fsmQJOnfujDVr1hSYcnXnzh3MnDlTP2reuXPnAo+X9xuqL774wugbFTm7d++Gr68vgoKCsHnz5gK13rt3D19++SVeeukl3L17F3Z2dhgxYkSB+//vf//Tr77x4osv4ttvvy3QxJ89exYLFixA/fr1cejQIYO1f/PNNxZ500waY5bVlIls3KMbhZjqcVtNN27cWL+gvrOzc6Gtpn/99VeDx929e3eBrVfd3d31O4K1bNlSTJw4UXYnvezsbP3GAnl/ypUrJzw8PPQbPADStr75WXKjEDmJiYmifv36+prs7OyEp6dngXMHIFatWlXgfqZuGPC4jUKEEGLTpk0Ftuf18PAosE3xK6+8YnSbYmM1mPIzfdxxjB0jICBAX6dOpxMVKlTQP8cDBw7U5/rR58WUuoz97NLT00WvXr0KPEceHh6P3Wr6888/L7CFtrOzs6hYsWKB7aaL89+aJZ/DvGzHxcUVuS5DxzH0c7l+/bp+9878z6Wbm1uBn0tERIT+PvPnzy9wXdmyZfU71OX9MbQr4eeff17g51+9enVRo0YNMXbsWJPOY+nSpQUeA5B2k8z/2pZ37Ef/3eZJSEgQ3t7e+tva29uLihUr6l/j8v7s3r27wP1OnTqlz4+9vb3w9vYWNWrUEK1atTKpdrItHEEmUqlKlSrht99+w7x58+Dn5wcHBwdkZmaidu3aCA0NxYkTJ2TX0G3Tpg2OHDmC3r17o1KlSsjIyICPjw8iIiLw888/G53qUaZMGSxevBj79u1DYGAgatSogczMTKSlpaF69erw9/dHTEwMvvvuOwuduemefvppHDlyBIsXL0a7du3g6emJe/fuwd7eHs8++yxGjhyJPXv2mPzhyOLo1q0bjh07hiFDhsDHxwepqalwdXXFSy+9hOjoaGzfvl0/n1Jt1q9fj//973+oX78+HBwcIIRAq1atEBMTg//7v/+z2OM6OTlh/fr1+P7779GtWzdUqVIFDx48gJeXF1q0aIGPP/5Y/0Gx/AYMGIC//voLoaGhaNCgAezt7XH37l395hJz586VnT5kjDU/h4C0FvaePXvw5ZdfomvXrqhcuTJSUlL0G7IMGjQIW7duxaRJk/T36dOnD5YvX47evXvrn/+UlBQ8+eST6N69O77//nuDU2AGDBiA5cuXo3nz5rC3t8elS5dw4cIFJCcnm1Tre++9h8OHD+Pjjz9Gly5dUKNGDWRlZSEtLQ0VK1ZEixYtMGnSJJw6dQr9+vUzeIxmzZrh9OnTmD17Nl588UWUK1cOd+7cgYuLC/z8/DBhwgQcPHiw0DJ19erVw86dO/Hqq6/C3d0d165dw4ULF/Tz8Yny0wnB2epERERERHk4gkxERERElA8bZCIiIiKifNggExERERHlwwaZiIiIiCgfe6UL0AovLy/4+PgoXQYVQXZ2tn5XJqL8mA0yhvkgOcyG9Tl//rzBVVj4LJqJj49PoUXJSd3CwsIwf/58pcsgFWI2yBjmg+QwG9Ynb7v2R3GZNzPx8/Njg0xERERkReT6N85BJpsVGBiodAmkUswGGcN8kBxmQzs4gmwmHEEmIiIisi4cQSZ6BN/pkxxmg4xhPkgOs6EdHEE2E44gExEREVkXjiATPSI4OFjpEkilmA0yhvkgOcyGdnAE2Uw4gmx9UlJS4ObmpnQZpELMBhnDfJAcZsP6cASZ6BHz5s1TugRSKWaDjGE+SA6zoR1skMlm9enTR+kSSKWYDTKG+SA5zIZ2sEEmmxUXF6d0CaRSzAYZw3yQHGZDO9ggk82qV6+e0iWQSjEbZAzzQXKYDe1gg0w2686dO0qXQCrFbJAxzAfJYTa0gw0y2ay0tDSlSyCVYjbIGOaD5DAb2sEGmWyWr6+v0iWQSjEbZAzzQXKYDe1gg0w2a//+/UqXQCrFbJAxzAfJYTa0gw0y2aazZxHQoYPSVZBK+fv7K10CqRjzQXKYDe1gg0y25fJloG9foHZtOL78MpCSonRFpEKLFy9WugRSMeaD5DAb2sGtps2EW02rXGYmsGABMH068ODBw8v79QNWrVKqKlKp7Oxs2NvbK10GqRTzQXKYDetjVVtN5+bmYv78+ahXrx6cnZ1RrVo1jB07Fg/yNzZGtG3bFjqdzuCfR38Iu3fvlr3t66+/bonTo9K2Ywfw7LPAhAlSc/zmm8DWrcgoUwaIiQFWr1a6QlKZwYMHK10CqRjzQXKYDe1Q5ducsLAwREZGwt/fH2PHjsWpU6cQGRmJI0eOYNeuXbCze3xf7+Xlhfnz5xe6vGbNmgZvP3ToULRu3brAZU899VTxToDU4fx5YMwY4Ntvpb/XqQNERgKdOwMAnJYtAwYPBoKDgRdekK4nArCKv1UgI5gPksNsaIfqGuQTJ04gKioKAQEB2Lhxo/5yX19fjBo1CuvWrTNpr/OyZcsiMDDQ5Mdt2bJlkW5PKpaWBnzyCTBrFpCeDpQtC3z4IRAaCjg66m8WFBeH1W+/DaxbB/TuDezfDzg5KVc3qUZQUBBW8zcLJIP5IDnMhnaoborF2rVrIYRAaGhogcuHDBkCV1dXxMbGmnys3Nxc3Lt3D6ZOs37w4AHS09OLUi6piRDApk1AgwbA1KlSc/zOO8BffwHjxxdojgFgdWwssGwZULMmcPSodBsigP/BkVHMB8lhNrRDdQ3ywYMHYWdnh+bNmxe43NnZGY0bN8bBgwdNOs7ly5fh5uYGd3d3uLm5ISAgAKdPn5a9/ejRo+Hm5gYXFxfUqVMHCxcuNLmxJhVITAReew144w3g3DmgYUNg925gzRqgalWDd+nfvz9Qvjywfj3g4CBNv9i0qVTLJnXq37+/0iWQijEfJIfZ0A7VNchXrlyBl5cXnAz8qrtq1apITk5GZmam0WP4+vpi/PjxWLlyJTZs2IDhw4dj27ZteOGFF3Ds2LECt3VwcED37t0xZ84cbNq0CUuXLoWHhwdCQ0MxcOBAo48THR0NPz8/+Pn54dy5c4iPj8emTZuwfv16JCQkIDIyEpcuXUJ4eDiys7P1/3CCgoIASP+QsrOzER4ejkuXLiEyMhIJCQlYv349Nm3ahPj4eERHRyMxMRHTpk1DSkoKgoODAUA/HSTva1hYGJKSkjBnzhwcO3YMMTEx2LFjB3bs2IGYmBgcO3YMc+bMQVJSEsLCwgweIzg4GCkpKZg2bRoSExMRHR2t+nOaP2MGUkNDkV2/PrBtG1IdHICFC/Fuo0ZAmzZGz6lTp07SOf36K25PnAgAEAMGYEzPnnyebPyc3N3dNXdOWnyelDqn3NxczZ2TFp8nJc6pd+/emjsnLT5P+c9JllCZmjVrimrVqhm8LigoSAAQt2/fLvJx4+PjhZ2dnejQocNjb5uTkyM6d+4sAIi9e/eadPymTZsWuSYqgdxcIdavF+Kpp4SQJlcIMWCAENeumXyIiRMnPvxLTo4QXbtKx3npJSGysixQNFmLAtkgegTzQXKYDesj17+pbgTZ1dUVGRkZBq/Lmx/s6upa5OO2bt0aL7/8MuLi4pCWlmb0tnZ2dggPDwcAbN26tciPRRZ24gTwyivSB+v+/Rdo2hT47Tfg88+BKlVMPszw4cMf/sXOTloP2dsb2LdPWi+ZbFaBbBA9gvkgOcyGdqiuQfb29kZycrLBJvny5cvw8vKC4yMftjKVj48PcnJycPv2bZNuCwDJycnFeiyygLt3pWXbnnsOiIsDPD2lD9kdOAC0aFHkw32bt/xbnkqVgC+/BHQ6YMYM4OefzVQ4WZtC2SDKh/kgOcyGdqiuQW7WrBlyc3ORkJBQ4PL09HQcPXoUfn5+xT52YmIi7O3t4enpadJtAaBKEUYkyUJyc4EvvgDq1gXmz5cmVAQHA2fOAEOHAmXKFOuwLQw11W3bAlOmSI/Rty9w40bJaierZDAbRP9hPkgOs6EdqmuQe/fuDZ1OhwULFhS4fPny5UhNTUXfvn31l129ehWnT59Gamqq/rK7d+8iJyen0HG3bNmCX375BR07doSzs7P+8ps3bxa6bUZGBiIiIgAA3bp1K+EZUYkcOQK0bi1tCX39OvDii8ChQ8DixUDFiiU69Llz5wxfMWUK8PLLwLVrQP/+UoNONkU2G0RgPkges6EdqtsopFGjRggJCcGiRYsQEBCArl276nfSa9OmTYFNQsLDwxETE4O4uDi0bdsWABAXF4cxY8agW7duqFmzJuzt7ZGQkIDY2Fh4eXkVarxfffVVeHt7o2nTpvD29saVK1cQGxuLxMREjBw5stByc1RKbt0CPvgAWLpUGs2tUkXa/CMwUJoCYQYuLi6Gr7C3l6ZaPPccsG0b8OmnwPvvm+UxyTrIZoMIzAfJYza0Q3UNMgAsWLAAPj4+iI6OxpYtW+Dl5YWRI0di+vTpj91mum7dumjatCl++OEHXL9+HVlZWXjqqacwbNgwTJo0CVUfWRO3R48e+O677xAVFYU7d+6gbNmyeP755zFt2jS88847ljxNMiQnB1ixApg8Gbh5U5o+MXq0tBOeu7tZH8rDw0P+yqeekj601707EB4ujSjzzZLNMJoNsnnMB8lhNrRDJwR3wzAHPz8/HDp0SOkyrNv+/cCIEcDvv0t/b99e2ryjQQOLPFx0dDSGDh1q/EahocDChYCvrzTdw8xNOqmTSdkgm8V8kBxmw/rI9W+qm4NMNkgIYO5caX7x779Lo7dffQXs2mWx5hgA2rVr9/gbzZ4NNGki7c43dKhUK2meSdkgm8V8kBxmQzvYIJOysrKAYcOAceOk5nP8eOD0aaBnT7PNNZazZs2ax9/IyQlYtw5wc5Oa9hUrLFoTqYNJ2SCbxXyQHGZDOzjFwkw4xaIY7tyRGuFduwBnZ2kpt/+2eS4NKSkpcHNzM+3GX34pfUDQ2VlaRcOCI9ukvCJlg2wO80FymA3rwykWpC7nzklTKnbtAipXBnbvLtXmGADGjRtn+o379gUGDADS04FevYB8SwuS9hQpG2RzmA+Sw2xoB0eQzYQjyEXw22/AG28ASUnSSOwPPwD/7Vyoag8eSNta//UXMGQIEB2tdEWF3b0rTVvx8lK6EiIiItXjCDKpw/r1QLt2UnPcqRPwyy+KNceBgYFFu0PZstI8ZCcnYPly6VzUIjUVmDoVeOIJac3ot96SRuX5/rdYipwNsinMB8lhNrSDI8hmwhHkxxAC+PhjaZc6QNoqOjJS2pTD2ixZAgwfDpQvLy39VrOmcrUIAWzYIG1kcumSdJm9PZCdLX3fqJG0dF7fvlKDT0RERHocQSblZGRIWzZPmSKtTDF/PvDZZ4o3x8V+pz9sGBAQANy7B7z9NpCZad7CTPXHH0DbtkDv3lJz3LgxEB8PXLz4cDT52DHgvfekpfPef1+a+02PxVEgMob5IDnMhnZwBNlMOIIs4+ZNqZmMjwdcXYG1a6Xd6azd7dvA888DFy5Ijecnn5TeYycnS282oqOB3FxpvvHHHwODBkk7D+bJzAS+/hqIipI2YQGkNyivvw6MHAl06GDxpfSIiIjUjCPIVPrOnAFatJCaY29vYO9eVTXHYWFhxb9zhQpSs1+mjLTJybZt5itMTnY2sGgRUKcOsHSp1NyOHi39nIcOLdgcA4CjI9Cnj/ShyIMHgXffBRwcgM2bpfnfzzwjjeTfv2/52q1MibJBmsd8kBxmQzs4gmwmHEF+xJ490sjxrVvSSOvmzUDVqkpXVUBSUhIqVapUsoP8739AeLg0ivvHH9IbAUv4+WepGT5+XPp7hw7AggVFX4/5xg3pA4ZLlgCXL0uXlS8vTYEZMQKoXducVVsts2SDNIv5IDnMhvXhCDKVni++ADp2lJrjbt2kEWSVNccAsHLlypIfZPx46VyTk6WNRHJySn7M/M6fl1akeOUVqTn29QW+/RbYsaN4m5VUrgxMnizNRf7qK6B1a2kudWSkNDLdpQuwdas0dcOGmSUbpFnMB8lhNrSDDTKZT24u8MEHQL9+0lq8YWFSM6fSXYW6dOlS8oPY2QGrV0tLq8XFAbNmlfyYgLTm8ocfAvXqAd98I83f/vhj4ORJ4M03Sz532MFB2pglPl5aiWPQIGmXwB9/BF57DahbVxqhvnvXHGdjdcySDdIs5oPkMBvawQaZzCMtTZrv+vHH0lzYxYuBTz8tPC9WRQ4fPmyeA1WpIjXJgLR6xN69xT+WEMC6dVJj/NFH0gogffpIm5NMmiQ1sebWuDGwYgXw77/A7NlA9erA2bPSG5yqVaUl7U6eNP/jqpjZskGaxHyQHGZDO9ggU8nduAG0by9tnFGunLQzXnCw0lU91pNPPmm+g3XsCEycKI2i9+kjrd5RVEePAm3aAO+8IzWrTZoA+/YBX34pLdNmaRUrSlNG/vlHGvlv104ayV6yRJrO0aED8P335p9GokJmzQZpDvNBcpgN7WCDTCVz8iTwwgvSMmLVqwO//gq8+qrSVSlj+nSgZUupuR040PRd7JKTpbWVmzaVRp8rVZI+SJeQALRqZdmaDSlTRprG8fPPD9dRdnUFfvpJuvzpp6WVO27dKv3aiIiISgEbZCq+nTulhvD8eaB5c+DAAaBhQ6WrMtnVq1fNe0AHB2DNGsDDA9i0SVp/2JjsbOk2tWsDy5ZJ85lDQ6Vl2wYPVsf0lIYNpSXl/v0XmDdP2jXw/Hlg3DhpVDs0VJpvrjFmzwZpCvNBcpgN7WCDTMWzfLm04sG9e0CPHtIH1J54QumqiqRJkybmP6iPjzSfF5CaSLn5aD/9JM39HTUKuHNHWpf4zz+lXQY9PMxfV0lVqACMGSM173nrKKelAQsXSlNK8ra21giLZIM0g/kgOcyGdrBBpqLJyZEav6FDpe8nTpTmHru6Kl1ZkW2z1OYeb70lzcHOzJS2os6/Ece5c9L60B06ACdOSCOy330nrR5Rv75l6jGnMmWknfi2b5c2IHF3l3brCwrSVJNssWyQJjAfJIfZ0A5uFGImNrFRyIMH0lq/330H2NtL0wIGDlS6qmKz6ILu6enS3Ow//5R+ZkuXSpuKfPKJtDJF2bLSesRhYZZZmaK0HDggfUDx/n2gb18gJkYdU0NKiIv9kzHMB8lhNqwPNwqhkrlyRVph4bvvpCkAO3ZYdXMMADNnzrTcwZ2dpeXaXF2B2FigRg1gxgypOQ4MlJZtCw+37uYYkN4E/PijtNb1l19K6ylrYJMRi2aDrB7zQXKYDe3gCLKZaHoE+Y8/pF+r//svUKuWtIxbvXpKV2UdVq58+EaiaVNpx7oXX1S2JkvYu1davSQ1VWqSo6OlDx0SERGpGEeQqXi2bAFeeklqjlu1kpZz00hzHBgYaPkH6d9fapK//FJatk2LzTEgbVm9ZQvg4gL83/9Jm4tY8XvvUskGWS3mg+QwG9rBEWQz0eQI8v79UlOcmyvNL/2//wOcnJSuitTsp5+k3zakpwMhIdIydiXdFpuIiMhCOIJMRRcbKzXHAwdKWylrrDnmO30LeOUVabc9Jyfgs8+kDyFa4XtwZoOMYT5IDrOhHRxBNhPNjSAL8XBTiN9+A1q0ULoisiZbtwL+/tJSd2PHSqt3cCSZiIhUhiPIVDR//SU1x15eQLNmSldjEcHBwUqXoF1du0rrIzs4SDvwhYdb1Ugys0HGMB8kh9nQDjbIZNiWLdLXV1/VxLq2hnzyySdKl6Bt3bpJm8jY2wOzZwMffqh0RSZjNsgY5oPkMBvawQaZDNu6VfratauydVjQvHnzlC5B+/z9gbVrpTdZM2YA06crXZFJmA0yhvkgOcyGdrBBpsLu3QPi46V1bDt3Vroai+nTp4/SJdiGHj2kD3za2QFTpwJWsJA+s0HGMB8kh9nQDjbIVNiuXUB2NtCyJeDpqXQ1FhMXF6d0Cbbj7belbah1OmmL7TlzlK7IKGaDjGE+SA6zoR1skKkwG5heAQD1NLLhidUIDJQ2TdHpgAkTgPnzla5IFrNBxjAfJIfZ0A42yFSQEA8b5NdeU7YWC7tz547SJdiefv2A5cul78eMkTYSUSFmg4xhPkgOs6EdbJCpoKNHgatXAW9v4Nlnla7GotLS0pQuwTYNGgQsXSp9P2oUsHixsvUYwGyQMcwHyWE2tIMNMhWUf3qFxjd28PX1VboE2/Xee8CiRdL3ISFAdLSy9TyC2SBjmA+Sw2xoBxtkKihv/WONT68AgP379ytdgm0LCQEWLJC+f+894PPPFS0nP2aDjGE+SA6zoR1skOmh5GRg/35p97NXXlG6Govz9/dXugQaPRqYO1f6fvBg4IsvlK3nP8wGGcN8kBxmQzvYINND27dLH9Jr0wYoV07paixusQrnvtqksWOBWbOk7A0YAKxZo3RFzAYZxXyQHGZDO3RCCKF0EVrg5+eHQ4cOKV1GyfTtKzUnn34KhIUpXY3FZWdnw97eXukyKM+MGcCUKdKGImvXAr16KVYKs0HGMB8kh9mwPnL9G0eQSZKTA/z4o/S9xtc/zjN48GClS6D8PvgA+PBDIDcX6NMH2LhRsVKYDTKG+SA5zIZ2cATZTKx+BPnXX4FWrYBatYDERM2vYEEqJYTUKM+cCdjbA19/DbzxhtJVERGRRnEEmYyzoeXd8gQFBSldAj1Kp5OmWowbJ2133rMn8MMPpV4Gs0HGMB8kh9nQDlU2yLm5uZg/fz7q1asHZ2dnVKtWDWPHjsWDBw9Mun/btm2h0+kM/jH0LuHu3bsYOXIkqlatCmdnZzRo0ABLliyBTQ2u29DybnlWr16tdAlkiE4HzJ4tzYPPygLeeuvh9J9SwmyQMcwHyWE2tEOVDXJYWBjGjBmDZ555BlFRUejZsyciIyPRrVs35ObmmnQMLy8vrF69utCfmjVrFrhdZmYmOnbsiKVLl6J3796IiopC3bp1MXz4cEybNs0Sp6c+ly9LO+i5uEgrWNiI/v37K10CydHpgHnzgJEjgcxM4M03gZ07S+3hmQ0yhvkgOcyGhgiVOX78uNDpdCIgIKDA5ZGRkQKA+PLLLx97jDZt2ogaNWqY9HifffaZACAiIyMLXB4QECAcHBzE+fPnTTpO06ZNTbqdKq1YIQQgxOuvK11JqcrKylK6BHqc3FwhgoOlfDo7C7FjR6k8LLNBxjAfJIfZsD5y/ZvqRpDXrl0LIQRCQ0MLXD5kyBC4uroiNjbW5GPl5ubi3r17RqdKrFmzBq6urhgyZEiBy0NDQ5GVlYX169cXqX6rZIPTKwBgypQpSpdAj6PTSVtSDx0KpKcD3bqVynQLZoOMYT5IDrOhHaprkA8ePAg7Ozs0b968wOXOzs5o3LgxDh48aNJxLl++DDc3N7i7u8PNzQ0BAQE4ffp0gdvk5ubi8OHDeP755+Hs7FzguubNm8POzs7kx7NamZkPf3VtI8u75Rk+fLjSJZAp7OyAJUuA4GAgI0Na1SLvTZ2FMBtkDPNBcpgN7VBdg3zlyhV4eXnBycmp0HVVq1ZFcnIyMjMzjR7D19cX48ePx8qVK7FhwwYMHz4c27ZtwwsvvIBjx47pb3f79m2kpaWhatWqhY7h5OSEihUr4vLly7KPEx0dDT8/P/j5+eHcuXOIj4/Hpk2bsH79eiQkJCAyMhKXLl1CeHg4srOz9XOT8j7l2r9/f2RnZyM8PByXLl1CZGQkEhISsH79emzatAnx8fGIjo5GYmIipk2bhpSUFAQHBwMAAgMDC3wNCwtDUlIS5syZg2PHjiEmJgY7duzAjh07EBMTg2PHjmHOnDlISkpC2H+bgAQGBgL79gEpKUDDhgieNQspKSmYNm0aEhMTER0dbZ3nlO9rcHCw7DlNmTJFc+ekxedp/fr1SDh0CJF16+J+//5AZiaEvz8Wtm9vsXMKCgri88Rzkj2nHj16aO6ctPg8KXFOs2bN0tw5afF5yn9Oskp1oocJatasKapVq2bwuqCgIAFA3L59u8jHjY+PF3Z2dqJDhw76yy5evCgAiKCgIIP3qVatmnjuuedMOr7VzkEeM0aa3zl+vNKVlLoDBw4oXQIVVW6uEGFhUmbt7YX4+muLPAyzQcYwHySH2bA+VjMH2dXVFRkZGQavS09P19+mqFq3bo2XX34ZcXFxSEtLK3AcY49XnMeyKvnXP7Yx586dU7oEKqq81S3Gj5fWSe7dG/jqK7M/DLNBxjAfJIfZ0A7VNcje3t5ITk422LRevnwZXl5ecHR0LNaxfXx8kJOTg9u3bwMAKlSoABcXF4PTKDIyMnDz5k2D0y80459/gNOnAXd34MUXla6m1Lm4uChdAhWHTgf873/ApEnSFunvvAOsWWPWh2A2yBjmg+QwG9qhuga5WbNmyM3NRUJCQoHL09PTcfToUfj5+RX72ImJibC3t4enpycAwM7ODk2aNMGRI0cKNeQJCQnIzc0t0eOpXt7ocadOgIODsrUowMPDQ+kSqLjydtybOhXIzQWCgoAvvjDb4ZkNMob5IDnMhnaorkHu3bs3dDodFixYUODy5cuXIzU1FX379tVfdvXqVZw+fRqpqan6y+7evYucnJxCx92yZQt++eUXdOzYscCKFe+88w5SU1MRHR1d4PYLFiyAvb09evXqZaYzUyEbnl4BoNCqJmRldDogIgL46COpSe7fH/j8c7McmtkgY5gPksNsaIe90gU8qlGjRggJCcGiRYsQEBCArl274tSpU4iMjESbNm3Qp08f/W3Dw8MRExODuLg4tG3bFgAQFxeHMWPGoFu3bqhZsybs7e2RkJCA2NhYeHl5FWq8hwwZgpUrV2LMmDE4f/486tevj61bt+Lbb7/FBx98AF9f31I8+1KUmgrExUnfd+mibC0KadeundIlkDl88AFgbw+EhwODBklzk4cOLdEhmQ0yhvkgOcyGdqhuBBmQRm/nzp2LEydOICQkBOvWrcPIkSPxww8/wM7OeMl169ZF06ZN8cMPP2Dy5MkYM2YM9u3bh2HDhuHo0aOoU6dOgds7Ojpi165deO+997B27VqEhITg9OnTiIqKwvTp0y15msqKi5M2XvDzA6pUUboaRawx87xVUtDEicDcudL3770HLF5cosMxG2QM80FymA3t0AlhZJs5Mpmfnx8OHTqkdBmmGz5c2nxh6lTp19Q2KCUlBW5ubkqXQea0cCGQtwvnwoXAqFHFOgyzQcYwHySH2bA+cv2bKkeQycKEsPn5xwAwbtw4pUsgcxs9WtqaOu/7Tz8t1mGYDTKG+SA5zIZ2cATZTKxqBPnkSaBBA6BSJeDaNWkrXyItiY6WploA0pJwEyYoWw8REakSR5DpoS1bpK9duth0c5y3FSVp0NChwP/9n7TSxcSJwMcfF+nuzAYZw3yQHGZDOziCbCZWNYLcrh2wezewbp20ExmRVsXEAAMGSNOKIiKADz+UmmYiIiJwBJny3L0L7NsHlCkjbRBiw/hO3wb06wesXi39piQiApgyRWqWH4PZIGOYD5LDbGgHR5DNxGpGkL/+GujZE2jdGoiPV7oaotKxfj3Qt6+0NfWECcCsWRxJJiIijiDTf7h6hV5YWJjSJVBp6d1bapLt7YHZs4H33zc6ksxskDHMB8lhNrSDI8hmYhUjyLm5gLc3cP068OefQKNGSlekqKSkJFSqVEnpMqg0ff+99BuUrCxpjeQFCwyOJDMbZAzzQXKYDevDEWQCjhyRmuOnngIaNlS6GsWtXLlS6RKotL3xBvDNN4CjIxAZCYwYIb1xfASzQcYwHySH2dAONsi2JG96xWuvcf4lgC5duihdAinh9deB774DnJykLamHDSvUJDMbZAzzQXKYDe1gg2xL8tY/5vxjAMDhw4eVLoGU0qULsGkT4OwMLF8ODB4sfYDvP8wGGcN8kBxmQzvYINuKpCQgIUH61XL79kpXowpPPvmk0iWQkjp1kt40uroCK1dK6yX/1yQzG2QM80FymA3tYINsK378UfrUftu2gJub0tUQqUP79tLUo7JlpfWS330XyM5WuioiIlIYG2RbweXdCrl69arSJZAatGkjvYF0cwPWrAH69sW1S5eUropUjK8dJIfZ0A42yLYgOxvYvl36/rXXlK1FRZo0aaJ0CaQWL70E7NgBlC8PfPUV3li7liPJJIuvHSSH2dAONsi2YP9+4PZtoHZt4Omnla5GNbZt26Z0CaQmLVsCO3cCHh5w/+knYMkSpSsileJrB8lhNrSDDbIt4PQKgwYMGKB0CaQ2zZtLH9gDgOnTgXv3lK2HVImvHSSH2dAONsi2IP/6x6Q3c+ZMpUsgNXrjDfzj7Q0kJwOffKJ0NaRCfO0gOcyGdnCraTNR7VbT//4LVKsmfUr/5k1pcwQiMu6334AXXwRcXICzZ6Ut2omISHO41bStypsP1aEDm+NHBAYGKl0CqVTgZ58BAQFAWhoQEaF0OaQyfO0gOcyGdnAE2UxUO4L85pvA998Dy5YBQ4cqXQ2R9fjrL6BBA2n98OPHgfr1la6IiIjMjCPItigjA9i1S/qe+8MXwnf6JCcwMBCoW1d6U5mbC0ycqHRJpCJ87SA5zIZ2cATZTFQ5grxrF9CxI/Dss8AffyhdDZH1uX4dqFULePAA2LtXWi+ZiIg0gyPItmjLFukrl3czKDg4WOkSSKX02ahSBXj/fen7ceOk6RZk8/jaQXKYDe3gCLKZqHIEuW5d4MwZID4eaN1a6WpUJyUlBW5ubkqXQSpUIBv370ub7Fy/Dnz9NfDWW8oWR4rjawfJYTasD0eQbc3Zs1Jz7OEh7RBGhcybN0/pEkilCmSjXDlg6lTp+/BwICtLmaJINfjaQXKYDe1gg6xVeZuDdO4M2NsrW4tK9enTR+kSSKUKZWPwYKBOHSAxEVixQpmiSDX42kFymA3tYIOsVdw977Hi4uKULoFUqlA2HByAWbOk7yMipGkXZLP42kFymA3tYIOsRQ8eALt3AzqdNIJMBtWrV0/pEkilDGbD31+arnTjBsBfo9o0vnaQHGZDO9gga9HPP0trIDdrBlSurHQ1qnXnzh2lSyCVMpgNnQ6YM0f6fu5c4Nq1Uq2J1IOvHSSH2dAONshaxOkVJklLS1O6BFIp2Wy89BLwxhvSb2mmTSvdokg1+NpBcpgN7WCDrDVCcP1jE/n6+ipdAqmU0WzMmgXY2QHLl0vbUZPN4WsHyWE2tIMNstacOAFcuiRtcNCkidLVqNr+/fuVLoFUymg26tcHBg0CcnKASZNKryhSDb52kBxmQzvYIGtN3uhxly7SKBfJ8vf3V7oEUqnHZiMiAnB1Bb75Bvj111KpidSDrx0kh9nQDnZQWpM3/5jTKx5r8eLFSpdAKvXYbHh7A2PGSN+PH88tqG0MXztIDrOhHdxq2kxUsdX0nTuAl5f0/c2bgLu7ouWoXXZ2Nuy5iQoZYFI27t0Dnn4aSEoCvvtO+vAe2QS+dpAcZsP6cKtpW7BjhzQv8qWX2BybYPDgwUqXQCplUjbKlwc+/FD6fuJEIDvbskWRavC1g+QwG9rBBllLuLxbkaxatUrpEkilTM7G0KFArVrA6dPA559btCZSD752kBxmQzvYIGtFbi6wbZv0PecfmyQoKEjpEkilTM6Go+PDLainTpXWRybN42sHyWE2tINzkM1E8TnIBw8CzZsD1asD589Lu34RkeUJAbRoASQkANOnA1OmKF0RERGZiHOQtS7/9Ao2xybp37+/0iWQShUpG/m3oJ4zB7hxwyI1kXrwtYPkMBvawRFkM1F8BLl5c2kUefNm4PXXlavDivDTxiSnWNno1g344QcgJARYtMgyhZEq8LWD5DAb1ocjyFp2/brUHDs5Ae3bK12N1ZjCX4WTjGJl43//kzbnWbYMSEw0f1GkGnztIDnMhnaoskHOzc3F/PnzUa9ePTg7O6NatWoYO3YsHhTzAzC9evWCTqdDw4YNC123e/du6HQ6g39et5aR2O3bpa/t2km7e5FJhg8frnQJpFLFykaDBkD//tJyb5Mnm70mUg++dpAcZkM7VNkgh4WFYcyYMXjmmWcQFRWFnj17IjIyEt26dUNubm6RjvXDDz9g48aNcHFxMXq7oUOHYvXq1QX+vP/++yU5jdKTt700V68okm+//VbpEkilip2NadMAFxdgwwbgwAHzFkWqwdcOksNsaIfqJsqcOHECUVFRCAgIwMaNG/WX+/r6YtSoUVi3bh369Olj0rFSUlIwfPhwhISEYNOmTUZv27JlSwQGBpaodkVkZz8cQeb6x0XSokULpUsglSp2Np56CggNlZZ+Gz8e2L2bH5rVIL52kBxmQztUN4K8du1aCCEQGhpa4PIhQ4bA1dUVsbGxJh9r8uTJyM7OxowZM0y6/YMHD5Cenl6UcpX366/A3btA3bpAzZpKV2NVzp07p3QJpFIlysaECUDFikB8/MPf7pCm8LWD5DAb2qG6BvngwYOws7ND8+bNC1zu7OyMxo0b4+DBgyYdJyEhAYsWLcKCBQtQvnz5x95+9OjRcHNzg4uLC+rUqYOFCxfCKhb44O55xfa4aTdku0qUDXf3h2shT5jALag1iK8dJIfZ0A7VNchXrlyBl5cXnJycCl1XtWpVJCcnIzMz0+gxsrOzMWTIEHTq1Am9evUyelsHBwd0794dc+bMwaZNm7B06VJ4eHggNDQUAwcONHrf6Oho+Pn5wc/PD+fOnUN8fDw2bdqE9evXIyEhAZGRkbh06RLCw8ORnZ2tXx8xb6ed/v37Izs7G+Hh4bh06RIiIyORkJCA9evXY9OmTYiPj0d0dDQSExMxbdo0pKSkIDg4GAD000EuLlsGAFh8/jySkpIwZ84cHDt2DDExMdixYwd27NiBmJgYHDt2DHPmzEFSUhLCwsIKHCPva3BwMFJSUjBt2jQkJiYiOjpakXPK+xoWFmbRczp8+LDmzkmLz5MS5xQbG1uycxo2DMnlygEnT2LTW2+p4py0+DwpdU5LlizR3Dlp8XlS4pwSExM1d05afJ7yn5MsoTI1a9YU1apVM3hdUFCQACBu375t9BgzZ84ULi4u4u+//9ZfVqNGDdGgQQOTasjJyRGdO3cWAMTevXtNuk/Tpk1Nup1ZXbggBCCEm5sQGRml//hWbtmyZUqXQCpllmysWSP9+/T2FuLBg5Ifj1SDrx0kh9mwPnL9m+pGkF1dXZGRkWHwurz5wa5GljI7e/Yspk+fjsmTJ6NmMefk2tnZITw8HACwNW8Kgxpt2yZ97dgRcHRUthYr1K5dO6VLIJUySzZ69waaNgWuXAEWLCj58Ug1+NpBcpgN7VBdg+zt7Y3k5GSDTfLly5fh5eUFRyPN4NixY+Hp6Ql/f3+cPXtW/yc7OxuZmZk4e/Ysrl69+tg6fHx8AADJycnFPheLy2veubxbsaxZs0bpEkilzJINO7uHW1DPng2o+bWEioSvHSSH2dAO1TXIzZo1Q25uLhISEgpcnp6ejqNHj8LPz8/o/S9cuIArV66gQYMGqF27tv7P5cuXkZiYiNq1a2PIkCGPrSPxv52wqlSpUvyTsaT0dGDXLun7Ll2UrcVKjR07VukSSKXMlo327YFXXwXu3QNMXE2H1I+vHSSH2dAO1TXIvXv3hk6nw4JHfiW5fPlypKamom/fvvrLrl69itOnTyM1NVV/2dy5c7Fhw4ZCfypVqoRq1aphw4YN+ukTAHDz5s1CNWRkZCAiIgIA0K1bN/OeoLnExwOpqUDjxkDVqkpXY5XGjRundAmkUmbNxuzZ0lrIixcD//xjvuOSYvjaQXKYDe3QCaG+tcxGjhyJRYsWwd/fH127dsWpU6cQGRmJVq1a4eeff4adndTX9+/fHzExMYiLi0Pbtm2NHtPHxwdubm44fvx4gcubNWsGb29vNG3aFN7e3rhy5QpiY2ORmJiIkSNHIjIy0qSa/fz8cOjQoWKdb7GMHg1ERgKTJgEff1x6j0tERde/PxATA7z9NrB2rdLVEBHRf+T6N9WNIAPAggULMHfuXJw4cQIhISFYt24dRo4ciR9++EHfHJtLjx49cOPGDURFRSE4OBiffvopqlatijVr1pjcHCuC6x+XmFXunEilwuzZmD4dcHIC1q0DSvONNFkEXztIDrOhHaocQbZGpTqCnJgI1KkDeHoCN24AZcqUzuMSUfFNmCB9aK9dO+Cnn7gFNRGRCljVCDI9Rt72tZ07szkuAb7TJzkWycbEiUCFCkBcHPDjj+Y/PpUavnaQHGZDO9ggWyNOrzCL2NhYpUsglbJINipUACZPlr6fMAHIyTH/Y1Cp4GsHyWE2tIMNsrVJSQH27JF+Pdu5s9LVWLW8rTKJHmWxbISEADVqAMeOAatXW+YxyOL42kFymA3t4BxkMym1Ocjffw+8+SbQsiXw66+WfzwNS0pKQqVKlZQug1TIotmIjQWCgoCnngLOnAFcXCzzOGQxfO0gOcyG9eEcZK3Yv1/6+sorytahAStXrlS6BFIpi2ajTx9p/fJ//wWioiz3OGQxfO0gOcyGdtgrXQAV0alT0teGDZWtQwO6cAdCkmHRbORtQd2pEzBzJjBoEFCxYsmOmZEB3LwpbWdt7GtmJvDpp8Czz5rnXGwUXztIDrOhHWyQrc3p09LXevWUrUMDDh8+jEaNGildBqmQxbPRsaP0Z+dOqUmeN+/hdWlpD5vaxzW8eV9TUkx/7MBA4PffAQcH85+XjeBrB8lhNrSDDbI1ycwEzp6VPqBXp47S1Vi9J598UukSSKVKJRuzZ0sNclSUtPRbXrObmlr0Y5UpA3h5SSPRj37N/31oqPQBwXnzpGXnqFj42kFymA3tYINsTc6elZaGqlmTH+whsnbPPw/06ydtQX3kyMPLHR0NN7rGvpYvb9rGI87O0tSOadOAHj2Ap5+23PkREVkxNsjWJG/+cf36ytahEVevXlW6BFKpUsvG0qVA//6Am9vDhtfNzXK77HXsKK2gsXo1MGyYNILNHf2KjK8dJIfZ0A6uYmFNOP/YrJo0aaJ0CaRSpZYNZ2egbVvAzw/w8QHKlbN8wzpvntSI//QT8MUXln0sjeJrB8lhNrSDDbI14QiyWW3btk3pEkilNJ2NSpWklSwAYMwYIClJ2XqskKbzQSXCbGgHG2RrwgbZrAYMGKB0CaRSms9GUJC0lvqtW1KTTEWi+XxQsTEb2sEG2Vrk5nKKhZnNnDlT6RJIpTSfDZ1Omv/s7Czt7Ldjh9IVWRXN54OKjdnQDm41bSYW32r64kWgRg2gcmXg+nXLPQ4R2Y7//Q8IDwd8fYHjxwFXV6UrIiIqVdxq2tpxeoXZBQYGKl0CqZTNZGPsWGlXvXPnpKXfyCQ2kw8qMmZDO9ggWws2yGYXGxurdAmkUjaTDQcHYPlyacrFvHnA0aNKV2QVbCYfVGTMhnawQbYWbJDNju/0SY5NZaN5c2DkSGkToiFDpK9klE3lg4qE2dAOzkE2E4vPQX75ZWDvXmD7dmknLCIic7l/H3jmGeDff4EFC4DRo5WuiIioVHAOsrXLW8GCI8hmExwcrHQJpFI2l41y5YDFi6XvJ0+WPhRMsmwuH2QyZkM7OIJsJhYdQb55U9qG1s0NuHePW8OaSUpKCtzc3JQug1TIZrPRsyfw9dfAa68BmzfztUaGzeaDHovZsD4cQbZmefOP69Xjf1hmNG/ePKVLIJWy2WxERgLu7sCWLcCGDUpXo1o2mw96LGZDO9ggW4P8DTKZTZ8+fZQugVTKZrPx5JPA7NnS96NGAbdvK1uPStlsPuixmA3tYINsDTj/2CLi4uKULoFUyqazMWQI8NJL0oZEEyYoXY0q2XQ+yChmQzvYIFsDLvFmEfU4Ik8ybDobdnZAdPTDNZLj45WuSHVsOh9kFLOhHWyQrQEbZIu4c+eO0iWQStl8NurXByZNkr4fOhTIyFC2HpWx+XyQLGZDO9ggq11qKnDhAmBvD9SqpXQ1mpKWlqZ0CaRSzAaA8HCgbl3gr7+AWbOUrkZVmA+Sw2xoBxtktTtzBhACePpp6VeeZDa+vr5Kl0AqxWwAcHKSploAwMyZD3+TRcwHyWI2tIMNstpxeoXF7N+/X+kSSKWYjf+8/LL0ob2sLGmqRW6u0hWpAvNBcpgN7WCDrHZskC3G399f6RJIpZiNfGbPBqpUAfbtA1asULoaVWA+SA6zoR1skNWOayBbzOK8rXWJHsFs5FOhgrSBCACMHw9cvapsPSrAfJAcZkM7uNW0mVhsq+lGjYDjx4GDBwE/P/Mf34ZlZ2fD3t5e6TJIhZiNRwgBdOsm7bDXo4fN77LHfJAcZsP6cKtpa5SdLX1ID+AIsgUMHjxY6RJIpZiNR+h0wOLFQNmywNdfA5s2KV2RopgPksNsaAdHkM3EIiPIiYlAnTpAtWrAxYvmPTYRUVEtXAiEhgJPPQWcPAmUK6d0RUREJcIRZGvE+ccWFRQUpHQJpFLMhowRI6SpXv/+C3zwgdLVKIb5IDnMhnawQVaz06elr1zBwiJWr16tdAmkUsyGjDJlpO2ny5QBoqKAhASlK1IE80FymA3tKFaDvHfvXixYsADz5s3D9u3bkZ2d/dj7hIWFYdCgQcV5ONvFJd4sqn///kqXQCrFbBjRuDEwdqz0wb28NZJtDPNBcpgN7SjSHOSrV6/irbfewoEDBwpcXqNGDcyfPx9vvPGG7H2ffPJJ3LhxAzk5OcWvVsUsMge5RQvgwAFg926gTRvzHpv4aWOSxWw8Rmoq0LAhcO4c8L//ARMmKF1RqWI+SA6zYX1KPAc5IyMDHTt2xIEDByCEgIODAzw9PSGEwPnz5xEQEIDRo0eDn/kzEyE4B9nCpkyZonQJpFLMxmO4ugJLl0rfR0QAf/+taDmljfkgOcyGdpjcIEdHR+PkyZMoW7YsVq1ahZSUFCQlJeH48eN44403IITAokWL0KtXL5OmXNBjXLsG3LsnLdJfubLS1WjS8OHDlS6BVIrZMEGnTkBgIJCeDgwbJr2ptxHMB8lhNrTD5AZ5w4YN0Ol0+N///od3331X/yuEZ555Bt9++y2WLVsGR0dHfPPNN+jevTvS09MtVrRNyD//WKdTthaN+vbbb5UugVSK2TDRp58CFSsCu3YBsbFKV1NqmA+Sw2xoh8kN8okTJwAA/fr1M3j9kCFD8OOPP6JcuXLYvn07unTpggcPHpinSlvED+hZXIsWLZQugVSK2TBRpUpSkwwAYWFAcrKy9ZQS5oPkMBvaYXKDfP/+fXh4eKBs2bKyt2nTpg127dqFChUqID4+Hh06dMC9e/eKXFRubi7mz5+PevXqwdnZGdWqVcPYsWOL3XD36tULOp0ODRs2NHj93bt3MXLkSFStWhXOzs5o0KABlixZoux8as4/trhz584pXQKpFLNRBEFBwCuvADdvAmPGKF1NqWA+SA6zoR0mN8gVKlTAvXv3kPWYJX38/PwQFxcHLy8vJCQkoF27drh582aRigoLC8OYMWPwzDPPICoqCj179kRkZCS6deuG3NzcIh3rhx9+wMaNG+Hi4mLw+szMTHTs2BFLly5F7969ERUVhbp162L48OGYNm1akR7LrLgGssXJZYKI2SgCnU76wJ6zM7B6NbBzp9IVWRzzQXKYDe0wuUF+5plnkJubi19//fWxt23UqBH27NmDJ554AkePHkXbtm2RlpZm0uOcOHECUVFRCAgIwDfffIMhQ4bg008/xaeffoq4uDisW7fO1JKRkpKC4cOHIyQkBJVlPui2YsUKHDx4UP8YQ4YMwTfffIOAgADMnDkTFy5cMPnxzIpTLCzOw8ND6RJIpZiNInr6aWDqVOn7YcOkZeA0jPkgOcyGdpjcIL/88ssQQpjcoNarVw/x8fGoVq0aTp48afJUi7Vr10IIgdDQ0AKXDxkyBK6urogtwgdBJk+ejOzsbMyYMUP2NmvWrIGrqyuGDBlS4PLQ0FBkZWVh/fr1Jj+e2dy9C1y5Io3I1KhR+o9vI07njdITPYLZKIaxY4FGjYB//gGmT1e6GotiPkgOs6EdJjfIeZuAxMbGIikpyaT71KpVC3v37sXTTz9tckEHDx6EnZ0dmjdvXuByZ2dnNG7cGAcPHjTpOAkJCVi0aBEWLFiA8uXLG7xNbm4uDh8+jOeffx7Ozs4FrmvevDns7OxMfjyzyvsHVreutKUrWUS7du2ULoFUitkoBgcHaRtqnQ6YOxf44w+lK7IY5oPkMBvaYXKD3KRJE8TGxmLRokW4e/euyQ9QrVo17Nu3D9OmTcOHH3742NtfuXIFXl5ecHJyKnRd1apVkZycjMzMTKPHyM7OxpAhQ9CpUyf06tVL9na3b99GWloaqlatWug6JycnVKxYEZcvX5a9f3R0NPz8/ODn54dz584hPj4emzZtwvr165GQkIDIyEhcunQJ4eHhyM7O1m9BGRQUBEDakjI7Oxvh4eG4dOkSIiMjkZCQgAMxMQCAGxUrIjo6GomJiZg2bRpSUlIQHBwMAAgMDCzwNSwsDElJSZgzZw6OHTuGmJgY7NixAzt27EBMTAyOHTuGOXPmICkpCWFhYQaPERwcjJSUFEybNg2JiYmIjo422zmtX78emzZtQnx8vGrOadKkSZo7Jy0+T0qc07vvvqu5cyqV50mnw9HWrYGcHFzq0gXZGRnWf04GnqeePXtq7py0+DwpcU7Tp0/X3Dlp8XnKf05yirTVdGmoVasWsrKycPHixULXvfvuu1i9ejVu375tdJ7PrFmz8NFHH+H48eOoWbMmAMDHxwdubm44fvy4/naXLl1C9erVERQUhC+++KLQcapXrw5PT08cPXr0sXWbdavpiROB2bOlHary5vWR2aWkpMDNzU3pMkiFmI0SuH8feOYZ4N9/gYULgVGjlK7I7JgPksNsWJ8SbzVdWlxdXZGRkWHwurzNR1xdXWXvf/bsWUyfPh2TJ0/WN8fGHguA0ccz9lgWww/olYpx48YpXQKpFLNRAuXKAZ99Jn3/wQfA1avK1mMBzAfJYTa0Q3UNsre3N5KTkw02rZcvX4aXlxccHR1l7z927Fh4enrC398fZ8+e1f/Jzs5GZmYmzp49i6v/vWBXqFABLi4uBqdRZGRk4ObNmwanX1gcG+RSsWTJEqVLIJViNkqoe3egWzdpNPn995WuxuyYD5LDbGhHiRvkoq5L/DjNmjVDbm4uEhISClyenp6Oo0ePws/Pz+j9L1y4gCtXrqBBgwaoXbu2/s/ly5eRmJiI2rVr61essLOzQ5MmTXDkyJFCDXlCQgJyc3Mf+3hml5EhfQrczg6oXbt0H9vG5M1RInoUs2EGCxdKK/GsWQPExSldjVkxHySH2dCOEjXIaWlp6N69u7lqAQD07t0bOp0OCxYsKHD58uXLkZqair59++ovu3r1Kk6fPo3UfGtuzp07Fxs2bCj0p1KlSqhWrRo2bNiA8PBw/e3feecdpKamIjo6usDjLViwAPb29kY/5GcRZ88COTmAr6/0nwtZTFGWDCTbwmyYga8vMHmy9H1ICPCYD1dbE+aD5DAbGiKK6datW6Jly5bCzs6uuIeQNWLECAFA+Pv7i+XLl4sxY8YIe3t70aZNG5GTk6O/Xb9+/QQAERcX99hj1qhRQzRo0KDQ5RkZGaJp06bC3t5ejBkzRixfvlz4+/sLAOKDDz4wueamTZuafFujNmwQAhDi9dfNczyS1bdvX6VLIJViNswkPV2I2rWl17T//U/pasyG+SA5zIb1kevf7IvTVF++fBmdOnXC6dOn0bt3b7M27IA0euvj44Po6Ghs2bIFXl5eGDlyJKZPnw47O/NOm3Z0dMSuXbvwwQcfYO3atbh58yZq1aqFqKgohISEmPWxTML5x6WG7/RJDrNhJk5O0gf2OnWSNg955x2genWlqyox5oPkMBvaUeRu8/Tp03jxxRdx6tQpvPHGGxYJQ5kyZTB27Fj89ddfyMjIwOXLl/Hpp58WWjpl1apVEEKgbdu2jz3m+fPnCyzxlp+HhwcWLVqEK1euICMjAydPnsSIESOg0+nMcTpFk7dJSL16pf/YNiZvHUeiRzEbZtSxI9Czp7T9tEZ+rswHyWE2tKNI6yDv378fr7/+Om7duoUuXbrg+++/h719sQahNcds6yA3aQIcOQL8+ivQsmXJj0eykpKSUKlSJaXLIBViNszs33+lN/0PHgBbtwJduihdUYkwHySH2bA+JV4HeevWrejQoQNu3bqF9u3b45tvvmFzbG65uQ9HkDnFwuJWrlypdAmkUsyGmT31lLTxEQCMHAn8t6a9tWI+SA6zoR0mN8hvvvkm0tLS0KpVK2zatMngVtBUQhcvAmlpwBNPAEZ2CiTz6GLlo1hkOcyGBYweDTRoAPz9t7RTqBVjPkgOs6EdJjfI2dnZAIAJEyYos7ucLeD841J1+PBhpUsglWI2LMDBAVi8WPp+1iypUbZSzAfJYTa0w+QGuU6dOhBCIDAwsNAmHmQmXMGiVD355JNKl0AqxWxYyMsvA0FB0oZIo0YBpn8ERlWYD5LDbGiHyQ3yL7/8gmbNmuHevXvo0qUL/vjjD0vWZZvYIBOR1n3yCeDuLn1Y77vvlK6GiMggkxvkihUrIi4uDp06dcLt27fRqVMnnDx50pK12R42yKXq6tWrSpdAKsVsWFCVKsDHH0vfjx4trWxhZZgPksNsaEeR1kF2dXXFDz/8gD59+iApKQkdOnTA2bNnLVWb7eEc5FLVpEkTpUsglWI2LGzYMGlJy0uXgBkzlK6myJgPksNsaEeRNwqxt7dHbGwsQkNDce3aNbRv394Sddme5GTpT7lyQNWqSldjE7Zt26Z0CaRSzIaFlSkjfWBPpwPmzXv42zMrwXyQHGZDO4q9b/Onn36KWbNm4d9//zVnPbYr7z+IevWk/zTI4gYMGKB0CaRSzEYpeOEFYPBgICsLCAmxqg/sMR8kh9nQjmI3yIC05BsXxTYTzj8udTNnzlS6BFIpZqOUzJoFVKwIxMUB69YpXY3JmA+Sw2xoR5G2miZ5Jd5qOiwMWLAAmDkTCA83W11ERKr2f/8njSQ/+aT0OYzy5ZWuiIhsSIm3miYL4xbTpS4wMFDpEkilmI1SNGAA0LIlcPUqMHWq0tWYhPkgOcyGdpTaCPKBAwcwY8YMbN68uTQertSVeATZxwe4cEFqlOvWNVtdRESqd/Qo0LSp9P3hw8BzzylaDhHZDsVGkOPj49GpUye8+OKL2Lp1q6Ufzjo9eCA1xw4OQM2aSldjM/hOn+QwG6WscWPpg3q5ucDw4dJXFWM+SA6zoR1FHkG+efMmNm7ciJMnTyInJwc1a9ZE79694e3tXeB2e/fuxeTJk/HLL78g7yGef/55/P777+arXkVKNIJ8+LA0elK/PsDNV4jIFt29K/327Pp14PPPpakXREQWZpYR5I0bN8LX1xfBwcGIiorC4sWL8f7776NmzZqIiYkBANy9exdvv/022rZti3379kEIgQ4dOmDHjh2abY5LjPOPFREcHKx0CaRSzIYC3N2lNZEBYPx44NYtZesxgvkgOcyGdpg8gnz69Gk0btwYmZmZAAA3NzcIIfDgv21Cy5Qpg/3792Pw4MH4448/UKZMGfTu3Rvjxo3DczYwn6xEI8hTpki7SU2ebJW7SlmrlJQUuLm5KV0GqRCzoRAhgPbtgd27gffeA5YuVboig5gPksNsWJ8SjyBHRUUhMzMTvr6++OWXX3Dv3j3cv38fe/fuhY+PD3JycvDqq6/ijz/+QOfOnXHy5EnExsbaRHNcYlwDWRHz8kariB7BbChEpwM++wywtweio4GDB5WuyCDmg+QwG9phcoO8Z88e6HQ6LFmyBC1bttRf3qpVKyxZsgQAcOvWLfTs2RPbtm1D7dq1zV+tVuXfRY9KTZ8+fZQugVSK2VDQM89I68ILAQQHAzk5SldUCPNBcpgN7TC5Qb548SLs7OzwyiuvFLrulVdegZ2ddKgPPvjAfNXZguxsIDFR+p4NcqmKi4tTugRSKWZDYR9+CDz1FPD778CyZUpXUwjzQXKYDe0wuUFOSUmBl5cXypQpU+g6e3t7eHl5AQDqsckrmn/+AbKygOrVgbJlla7GpjCrJIfZUJibm7SzKCB9NuPGDUXLeRTzQXKYDe0o0ioWOp3usdc5ODiUrCJbw/nHirlz547SJZBKMRsqEBAAdO4M3LkjrWqhIswHyWE2tINbTSuN848Vk5aWpnQJpFLMhgrodEBUFODoCMTEAHv3Kl2RHvNBcpgN7bAvyo1v3bqF9u3by14HQPZ6QBpl/umnn4rykNrHNZAV4+vrq3QJpFLMhkrUrg1MmAB89JG0w97hw9KOowpjPkgOs6EdRWqQMzMzsXv3bqO3MXa9sSkaNotTLBSzf/9+NG/eXOkySIWYDRUJDwdiY4Hjx6UR5TFjlK6I+SBZzIZ2mNwg9+vXz5J12CYh2CAryN/fX+kSSKWYDRVxcZEa49dfB6ZOBXr3BqpWVbQk5oPkMBvaYXKDvHLlSkvWYZuuXAHu3wc8PYH/VgGh0rN48WLMmjVL6TJIhZgNlXntNeCNN4DvvwfGjgXWrVO0HOaD5DAb2mHyVtNkXLG2mv7pJ6BDB6BVK2DfPssURrKys7Nhb1+kWUZkI5gNFbpwQfpNW1oasHOn9NqpEOaD5DAb1qfEW02TBXB6haIGDx6sdAmkUsyGCtWoAUyZIn0fEgJkZChWCvNBcpgN7WCDrCQ2yIpatWqV0iWQSjEbKjV2LFC3LnDmDDBvnmJlMB8kh9nQDjbISuIayIoKCgpSugRSKWZDpRwdgc8+k76fMQM4f16RMpgPksNsaAfnIJtJseYge3sDV69K201z7UQiItO8/Tawfj3Qvbv0wT0iomLiHGS1uXtXao5dXKS5dVTq+vfvr3QJpFLMhsrNmwe4uQGbNgE//FDqD898kBxmQzs4gmwmRR5B3r8faNkSaNwYOHLEYnWRPH7amOQwG1Zg/nxp0xBfX+DECWmwoZQwHySH2bA+HEFWG84/VtyUvE/EEz2C2bACI0cCjRoB584BpbzuLPNBcpgN7WCDrJTTp6WvXMFCMcOHD1e6BFIpZsMK2NsDixdL38+eLa1sUUqYD5LDbGgHG2SlcIk3xX377bdKl0AqxWxYiZdeAvr1AzIzgfbtpQ1ESgHzQXKYDe1gg6wUNsiKa9GihdIlkEoxG1Zk3jygRQvg8mWgUydp6kVqqkUfkvkgOcyGdrBBVkJ6urS0m50dULu20tXYrHPnzildAqkUs2FFKlYE9u6V1kW2twcWLQKaNAEOHrTYQzIfJIfZ0A42yEo4exbIzQVq1gScnJSuxma5lOKn3sm6MBtWxt4emDwZOHAAeOYZ4K+/pFWCIiKArCyzPxzzQXKYDe1gg6wETq9QBQ8PD6VLIJViNqxUkybA778DYWFATg4wbRrw4osPPxRtJswHyWE2tEOVDXJubi7mz5+PevXqwdnZGdWqVcPYsWPx4MGDx943KysLw4YNQ9OmTeHl5QUnJyf4+vqid+/eOGJgveHdu3dDp9MZ/PP6669b4vTYIKvEaTP/p0nawWxYMWdn4NNPgZ9/BqpXBw4dAp5/HoiMlH5zZwbMB8lhNrRDlatZh4WFITIyEv7+/hg7dixOnTqFyMhIHDlyBLt27YKdnXxfn5mZiUOHDqFVq1YICgpCuXLlcPHiRaxcuRIvvPACfvzxR7Rv377Q/YYOHYrWrVsXuOypp54y+7kB4BrIKtGuXTulSyCVYjY0oF074M8/gdGjgZgY6evmzcDnnwPVqpXw0MwHGcZsaIhQmePHjwudTicCAgIKXB4ZGSkAiC+//LJYx71y5Yqwt7cXXbp0KXB5XFycACBWrlxZ3JKFEEI0bdrU9Bs3biwEIMRvv5XoMalkIiIilC6BVIrZ0JiNG4Xw8pJed93dhYiNFSI3t9iHYz5IDrNhfeT6N9VNsVi7di2EEAgNDS1w+ZAhQ+Dq6orY2NhiHbdy5cpwdnbG7du3ZW/z4MEDpKenF+v4JsvNlT5AAnCKhcLGjh2rdAmkUsyGxgQEAMeOAa+/Dty9CwQGAr17AzdvFutwzAfJYTa0Q3UN8sGDB2FnZ4fmzZsXuNzZ2RmNGzfGQROX7snJyUFycjKuXbuGgwcPok+fPkhJSUHXrl0N3n706NFwc3ODi4sL6tSpg4ULF0IIUeLzKeTCBSAtDXjyScDd3fzHJ5ONGzdO6RJIpZgNDXriCWDTJmD5csDNDdiwAWjYENi2rciHYj5IDrOhHaprkK9cuaL/cN2jqlatiuTkZGRmZj72OKdOnUKlSpXw5JNPonnz5ti+fTvCw8MRHh5e4HYODg7o3r075syZg02bNmHp0qXw8PBAaGgoBg4caPQxoqOj4efnBz8/P5w7dw7x8fHYtGkT1q9fj4SEBERGRuLSpUsIDw9HdnY2+vfvX2D+cf/+/ZGdnY3w8HBcunQJkZGRSEhIwPr167Fp0ybEx8cjOjoaiYmJmDZtGlJSUhAcHAwACAwMLPA1LCwMSUlJmDNnDo4dO4aYmBjs2LEDO3bsQExMDI4dO4Y5c+YgKSkJYWFhBo8RHByMlJQUTJs2DYmJiYiOjjbtnAAEBQUBgFWdU9u2bTV3Tlp8npQ4J2dnZ82dkxafpyKfk06HwN27gT/+wF+VKgHXrgFduyJr0CDMnDTJ5HNKS0tTzzlp8Xmy4nPy9/fX3Dlp8XnKf06ySnWihwlq1qwpqlWrZvC6oKAgAUDcvn37scdJSUkRO3fuFFu2bBELFy4UzZo1E8OHDxcpKSmPvW9OTo7o3LmzACD27t1rUt0mz0GeN0+aBzd8uGm3J4vp27ev0iWQSjEbNiA7W4jZs4VwcJBek2vVEuLXX026K/NBcpgN62M1c5BdXV2RkZFh8Lq8+cGurq6PPU7ZsmXRoUMHdO3aFaNGjcLPP/+MnTt3IiAg4LH3tbOz0480b926tQjVm4BLvKlGceezk/YxGzagTBlg/HhpGbhGjYC//wZeeknacOQxv6VkPkgOs6EdqmuQvb29kZycbLBJvnz5Mry8vODo6Fjk47q5uSEgIAA7duzA33///djb+/j4AACSk5OL/FhGsUFWjbxfwRA9itmwIc8+K21LPX48IAQwcybQogVw4oTsXZgPksNsaIfqGuRmzZohNzcXCQkJBS5PT0/H0aNH4efnV+xj580bu3Xr1mNvm5iYCACoUqVKsR+vECG4BrKK8J0+yWE2bIyTEzB7NrBnD+DrCxw5AjRtKm04YmBzEeaD5DAb2qG6Brl3797Q6XRYsGBBgcuXL1+O1NRU9O3bV3/Z1atXcfr0aaSmpuovS0pKQq6BF7Rr165hw4YNcHNzQ4MGDfSX3zSwzE9GRgYiIiIAAN26dSvhGeWTnAzcugWUKwd4e5vvuFQseR8yIHoUs2GjWrcG/vgDGDQIyMgAxo4F2reXVh/Kh/kgOcyGduiEsMRaZiUzcuRILFq0CP7+/ujatat+J71WrVrh559/1u+k179/f8TExCAuLg5t27YFACxYsAALFiyAv78/fH194ejoiDNnziAmJga3b9/GihUrCqxO0axZM3h7e6Np06bw9vbGlStXEBsbi8TERIwcORKRkZEm1ezn54dDhw4Zv1F8PNCmDdC8OXDgQLF+NmQ+SUlJqFSpktJlkAoxG4TNm4HBg4EbN6RBjchIoF8/QKdjPkgWs2F95Po31Y0gA1KTO3fuXJw4cQIhISFYt24dRo4ciR9++MHoNtMA0Lp1a7Rq1QqbN2/GpEmTMGrUKGzYsAEdOnTAvn37Ci3d1qNHD9y4cQNRUVEIDg7Gp59+iqpVq2LNmjUmN8cm4/xjVVm5cqXSJZBKMRuEbt2A48cBf3/g/n1gwADgrbeApCTmg2QxG9qhyhFka2TSCHJoKLBwITBrFjBxYqnURfKOHTuGRo0aKV0GqRCzQXpCAF98AYwcKTXKlSvjnxkzUHPIEKUrIxXia4f1saoRZM06fVr6yhFkVTh8+LDSJZBKMRukp9NJUyuOHQPatgVu3ED1kBBpyhzRI/jaoR1skEsTp1ioypNPPql0CaRSzAYVUqMG8NNPwHvvwT4rS5qCcfSo0lWRyvC1QzvYIJeWlBTg4kXAwQGoWVPpaoiIqKjs7IDPPsO11q2Be/eAzp2B/5YEJSJtYYNcWv76S/pauzZgb69sLQRAWiaQyBBmg2SVKYOd774LdOworXDRqRNw5YrSVZFK8LVDO9gglxZOr1CdJk2aKF0CqRSzQcY0fuEF4JtvpCU7z5+XRpJN2ICKtI+vHdrBBrm08AN6qrNt2zalSyCVYjbImG3btgFubsDWrdJr+vHjwOuvAw8eKF0aKYyvHdrBBrm0cARZdQYMGKB0CaRSzAYZo89HxYrAjh1A9erAb78BPXoAmZnKFkeK4muHdrBBLi15DXK9esrWQXozZ85UugRSKWaDjCmQj6eeAnbuBCpVAn78UVoSLjdXueJIUXzt0A5uFGImRjcKycoCXF2B7GxpNYuyZUu3OCIisqzffwfatZM2EwkJAaKipDWUiUjVuFGIkv75R2qOa9Rgc6wigYGBSpdAKsVskDEG89G0KbBpE+DkBHz2GTBtWukXRorja4d2cATZTIyOIH/3HeDvD7z6KsAJ/ERE2vXdd8Bbb0nTLCIjpS2qiUi1OIKsJM4/ViW+0yc5zAYZYzQfb74JrFghfT9qFPDll6VSE6kDXzu0gw1yaeAKFqoUGxurdAmkUswGGfPYfAwYAHzyifR9//7ScnBkE/jaoR1skEsD10BWpeDgYKVLIJViNsgYk/Lx/vvAhAnS50969AD27bN8YaQ4vnZoB+cgm4nsHGQhAHd36ZPNSUmAl1fpF0cGpaSkwM3NTekySIWYDTLG5HwIAQwZAvzf/0n/D8THA88+a/kCSTF87bA+nIOslMuXpea4YkU2xyozb948pUsglWI2yBiT86HTAUuXAgEBwN270pbUf/9t2eJIUXzt0A42yJbG+ceq1adPH6VLIJViNsiYIuXD3l76oF779sC1a0CnTsDVq5YrjhTF1w7tYINsaZx/rFpxcXFKl0AqxWyQMUXOh7OztPybn5+0Ln7nzsDt2xapjZTF1w7tYINsaRxBVq16XHaPZDAbZEyx8lGunLSaRd26wLFjQLduQGqq+YsjRfG1QzvYIFsa10BWrTt37ihdAqkUs0HGFDsflSoBO3YATz0F/PIL0LMnkJVl1tpIWXzt0A42yJbGEWTVSktLU7oEUilmg4wpUT6qV5ea5IoVpRHlAQOkXfdIE/jaoR1skC3p9m3g+nXA1VV6USRV8fX1VboEUilmg4wpcT7q1we2bQPc3KQP8IWGSkvCkdXja4d2sEG2pLwP6NWtC9jxR602+/fvV7oEUilmg4wxSz6aNZM+uOfoCERFATNmlPyYpDi+dmgHuzZL4vxjVfP391e6BFIpZoOMMVs+XnkFWLtWGkD58ENg8WLzHJcUw9cO7WCDbEmcf6xqi/mfEclgNsgYs+YjIABYtkz6fsQIYN068x2bSh1fO7SDW02bicGtCrt1A374AdiwAejRQ5nCSFZ2djbs7e2VLoNUiNkgYyySj9mzgYkTpY1FNm8GXn3VvMenUsHXDuvDraaVwBFkVRs8eLDSJZBKMRtkjEXyMX488P77QHY28NZbwG+/mf8xyOL42qEdHEE2k0LvQNLTgbJlAZ0OePAAcHJSrjgiIlI/IYBBg4CVK4EKFYD4eKBhQ6WrItI0jiCXtjNnpLUta9Zkc6xSQUFBSpdAKsVskDEWy4dOB0RHA2+8IS0T2qkTcO6cZR6LLIKvHdrBBtlS8pZ44/QK1Vq9erXSJZBKMRtkjEXzYW8vfVCvTRvg6lWgcWNgyhTg5k3LPSaZDV87tIMNsqVw/rHq9e/fX+kSSKWYDTLG4vlwdgY2bQK6dAHu3ZPWSPbxASZNApKTLfvYVCJ87dAONsiWwgZZ9VasWKF0CaRSzAYZUyr5KF9e2or6l1+Azp2BlBRg1iypUR4/Hrhxw/I1UJHxtUM72CBbCjcJUb0pU6YoXQKpFLNBxpRqPl58EfjxR2lVi65dpQ99f/KJ1CiPHQtcu1Z6tdBj8bVDO7iKhZkU+BRkTg7g5iatZHHnDuDurmhtZNilS5dQrVo1pcsgFWI2yBhF83HoEDB9urRWMiBNx3jvPWlU2dtbmZpIj68d1oerWJSmCxek5tjbm82xin377bdKl0AqxWyQMYrmw89Pmp98+DDw5pvS/zULF0orJo0cCfz7r3K1EV87NIQNsiVw/rFVaNGihdIlkEoxG2SMKvLx/PPAt98CR49KO7VmZACLFgG1agHBwcDFi0pXaJNUkQ0yCzbIlsD5x1bhHNcXJRnMBhmjqnw89xywYQNw7BjQuzeQlQUsXQo8/TQwdChw/rzSFdoUVWWDSoQNsiVwDWSr4OLionQJpFLMBhmjynw0bCitn3z8ONCnj/RZmOXLgdq1pd35/v5b6QptgiqzQcXCBtkSOMXCKnh4eChdAqkUs0HGqDofzzwDfPklcPIkEBQk7ej6+edA3bpA//5AYqLSFWqaqrNBRcIG2dyEYINsJU7njfQTPYLZIGOsIh916wJffAH89ZfUGANATIw09S8o6OFvOsmsrCIbZBI2yOZ24wZw+7a0yPsTTyhdDRnRrl07pUsglWI2yBirysfTTwMrVwJnzkhTLezsgNhYaaS5Tx9ppJnMxqqyQUaxQTa3/POPdTplayGj1qxZo3QJpFLMBhljlfmoWRNYsUKaYvHee4C9PbB2rTR3uVcv6UN+VGJWmQ0ySJUbheTm5mLhwoVYtmwZzp8/j0qVKqFXr16YPn06ypYta/S+WVlZGDlyJA4ePIgLFy7g/v378Pb2RvPmzTFx4kQ8//zzhe5z9+5dfPDBB/jmm29w8+ZN1KpVCyNGjMCwYcOgM7HJ1S80vXSptMRO//7Su3ZSrZSUFLi5uSldBqkQs0HGaCIfFy8Cs2dLTXNmpnRZo0bS2v3lykmbXZn6Nf/3Tk42PTikiWzYGLmNQuwVqOWxwsLCEBkZCX9/f4wdOxanTp1CZGQkjhw5gl27dsHOTn7gOzMzE4cOHUKrVq0QFBSEcuXK4eLFi1i5ciVeeOEF/Pjjj2jfvn2B23fs2BFHjhzByJEjUb9+fWzbtg3Dhw/H9evXERERUbTiOf/YaowbNw5LlixRugxSIWaDjNFEPqpXBz77DAgPB+bMAaKjzTOKbG9vWlPdoIE0xcPRseSPqSKayAYBUOEI8okTJ9CoUSP4+/tj48aN+sujoqIwatQofPnll+jTp0+Rj3v16lVUr14dHTt2xNatW/WXL168GCEhIYiMjMTIkSP1l7/11lvYvHkzEhMTUaNGjcceX/8OpFMnYOdO4Pvvge7di1wnERFRqbt1S1oz+f59ICWl8FdDlxn6mpVl+mNWrw5Mniz9xlVjjTJZD7kRZNU1yB988AE+/vhjxMfHo3Xr1vrL09PTUbFiRbRp06ZAg2uqnJwceHh4oGHDhvjtt9/0l7/00ks4cuQIbt68CWdnZ/3le/fuxcsvv4zZs2dj/Pjxjz2+/gdcvTpw6ZL0gYjatYtcJ5WewMBAxMbGKl0GqRCzQcYwH0ZkZj6+qb5z5+FSdICmGmVmw/pYTYPcuXNn7Nq1C6mpqXBycipwXatWrXDmzBkkJSU99jg5OTm4ffs2srOzcenSJcydOxdfffUVpk+fjilTpgCQ5jq7ubmhSZMm2LdvX4H7Z2RkwNXVFQEBAdiwYcNjH8/Pzw+Hdu+Wfn3k6Ag8eCD9qomIiIgKys0Fvv4amDZNk40yWQ+5Bll1q1hcuXIFXl5ehZpjAKhatSqSk5ORmfeBAiNOnTqFSpUq4cknn0Tz5s2xfft2hIeHIzw8XH+b27dvIy0tDVWrVi10fycnJ1SsWBGXL1+WfYzo6Gj4+fnBz88P586dw+H/Pr16p0oVJBw+jMjISFy6dAnh4eHIzs5G///WogwKCgIA9O/fH9nZ2QgPD8elS5cQGRmJhIQErF+/Hps2bUJ8fDyio6ORmJiIadOmISUlBcHBwQCkd6n5v4aFhSEpKQlz5szBsWPHEBMTgx07dmDHjh2IiYnBsWPHMGfOHCQlJSEsLMzgMYKDg5GSkoJp06YhMTER0dHRiI+Px6ZNm7B+/XokJCRo6pxeeuklzZ2TFp8nJc6pfv36mjsnLT5PSp1TrVq1NHdOpf487dqFHR4eiHn/fVz85BMkV64sfXDwvfeA2rXxfy+8AGRmWtc57diBV155RVvPkxaz98g5yVHdCHKtWrWQlZWFixcvFrru3XffxerVq3H79u3H7lbz4MED/Pbbb8jMzMTZs2cRGxuLZs2aYc6cOfqVMC5duoTq1asjKCgIX3zxRaFjVK9eHZ6enjh69Ohj6/bz88Oh0aOBd98FevQATBh1JiIiInBEmRRjNSPIrq6uyMjIMHhdenq6/jaPU7ZsWXTo0AFdu3bFqFGj8PPPP2Pnzp0ICAgo8FgAjD6eKY+lxxUsrEreO2CiRzEbZAzzYQF2dg/XY16/XtrIJN+IMqKjHy5Hp2LMhnaorkH29vZGcnKywab18uXL8PLygmMx3km6ubkhICAAO3bswN9//w0AqFChAlxcXAxOo8jIyMDNmzcNTr+QlX+TEFK9SZMmKV0CqRSzQcYwHxaU1yj/+Sewbp3VNcrMhnaorkFu1qwZcnNzkZCQUODy9PR0HD16FH5+fsU+dlpaGgDg1q1bAAA7Ozs0adIER44cKdSQJyQkIDc3t2iPxxFkq7KSG7mQDGaDjGE+SkGZMkDv3lbXKDMb2qG6Brl3797Q6XRYsGBBgcuXL1+O1NRU9O3bV3/Z1atXcfr0aaSmpuovS0pKQm5ubqHjXrt2DRs2bICbmxsaNGigv/ydd95BamoqoqOjC9x+wYIFsLe3R69evUwrXAjg7FlpB6E6dUy7DymqS5cuSpdAKsVskDHMRymyskaZ2dAO1TXIjRo1QkhICL755hsEBARgxYoVGDt2LMaMGYM2bdoU2CQkPDwc9evXLzDa/OWXX6JmzZr63fiWLl2KMWPGoEGDBrh27RoWLlxYYF7xkCFD0LRpU4wZMwZjx47FihUrEBAQgG+++QYTJ06Er6+vaYVnZADZ2UCNGkBR5i2TYg4fPqx0CaRSzAYZw3wowFijXKcOsHy5KhplZkM7VLlQ74IFC+Dj44Po6Ghs2bIFXl5eGDlyJKZPn250m2kAaN26NQ4ePIjNmzfj2rVryMzMRJUqVdChQweMHj0aL774YoHbOzo6YteuXfjggw+wdu1a3Lx5E7Vq1UJUVBRCQkJML/q/DxByeoX1ePLJJ5UugVSK2SBjmA8F5TXKPXo8XPXi1Clg6FDg44+lVS/69VNs1QtmQztUt8ybtfKrWhWHrlwBxowB5s1TuhwywY4dO9CpUyelyyAVYjbIGOZDRXJyCjbKgPSbXIUaZWbD+ljNMm9WK28EuV49Zesgk129elXpEkilmA0yhvlQkbwR5WPHpKkX9esDFy5II8oKTL1gNrSDDbK5cIqF1WnSpInSJZBKMRtkDPOhQsYa5WeeAQ4eLJUymA3tYINsLmyQrc62bduULoFUitkgY5gPFXu0Ua5XD/j7b6BVKyAyUlpxyoKYDe3gHGQz8dPpcKhSJeDGDaVLIRMlJSWhUqVKSpdBKsRskDHMhxXJyADGjQOioqS/v/UW8H//B7i7W+ThmA3rwznIpYHzj63KzJkzlS6BVIrZIGOYDyvi5CSNHG/YAJQvD2zcCDRpAlhoOTZmQzs4gmwmfjodDg0dCixbpnQpRERE9Ki//wZ69gSOHJFWt5g/HwgOljb4IpvFEeTSwPnHViUwMFDpEkilmA0yhvmwUrVqAb/+KjXFmZlASAjwzjvAvXtmewhmQzs4gmwmfr6+OPTjj0DdukqXQkRERMasXw8MHgykpEhbVn/1FdC4sdJVkQI4gmxpFSuyObYyfKdPcpgNMob50IDevYHffweefRZITARatACio0u8ygWzoR0cQTYTuXcgREREpFJpaUBoqNQcA0CfPtJnidzcFC2LSg9HkIkeERwcrHQJpFLMBhnDfGiIi4vUEMfGAmXLAmvWAH5+0jrKxcBsaAdHkM2EI8jWJyUlBW4cJSADmA0yhvnQqNOnpVUujh8HnJ2Bzz4DBgwo0ioXzIb14Qgy0SPmzZundAmkUswGGcN8aFS9esCBA1JTnJ4ODBoE9O8PPHhg8iGYDe1gg0w2q0+fPkqXQCrFbJAxzIeGuboCn38OrFolTb/44gugWTPgxAmT7s5saAcbZLJZcXFxSpdAKsVskDHMhw3o1w84eFDa3+DUKaB5cyAm5rF3Yza0gw0y2ax63BqcZDAbZAzzYSMaNJCa5KAgIDVVmm4xaJD0vQxmQzvYIJPNunPnjtIlkEoxG2QM82FDypaVRo5XrJA+uPf558ALL0gf6DOA2dAONshks9LS0pQugVSK2SBjmA8bo9NJI8cJCdKGYMePS0vBfflloZsyG9rBBplslq+vr9IlkEoxG2QM82GjGjWSplz06SOtbBEYCAwdKm028h9mQzvYIJPN2r9/v9IlkEoxG2QM82HDypWTNhVZtgxwcgKWLwdatgTOnAHAbGgJG2SyWf7+/kqXQCrFbJAxzIeN0+mkkeP9+4Gnnwb++ANo2hRYv57Z0BA2yGSzFi9erHQJpFLMBhnDfBAAoHFj4Pffpd33UlKAt9/Gle7dgcuXla6MzIBbTZsJt5q2PtnZ2bC3t1e6DFIhZoOMYT6oACGAJUuAsDAgMxOwtwcCAoARI4CXXirSVtVU+rjVNNEjBg8erHQJpFLMBhnDfFABOh0wfDiQkICEGjWkhvmrr4CXXwaefx74v/8zunYyqRNHkM2EI8hERESEf/+VPsS3bBmQlCRd5ukpLRU3fDjg46NoeVQQR5CJHhEUFKR0CaRSzAYZw3yQnKCgIOCpp4CPPgIuXQK++AJo1gy4dQv45BOgZk3gjTeAXbukkWZSLY4gmwlHkImIiMighAQgKgpYvx7IypIuq1dPmqf87rvS8nGkCI4gEz2if//+SpdAKsVskDHMB8mRzUbz5sDq1dKo8kcfAd7e0nbVI0ZII86jR+vXUiZ14AiymXAE2frwk+gkh9kgY5gPkmNyNrKygG+/BRYtAvbufXh5587AyJFAly6AHccwSwNHkIkeMWXKFKVLIJViNsgY5oPkmJwNBwegVy8gPh44ckT6AJ+zM7B9O/D660Dt2sCnnwJ37li0XpLHEWQz4Qiy9bl06RKqVaumdBmkQswGGcN8kJwSZePWLWlJuMWLgfPnpctcXYGgIGkqRsOGZquTHuIIMtEjvv32W6VLIJViNsgY5oPklCgbnp7AuHHA2bPA998DHTpI6ycvWwY0agS0awds3AhkZ5uvYJLFBplsVosWLZQugVSK2SBjmA+SY5ZslCkDdO8O7NwJnDwJhIQAbm7A7t1Ajx7SUnGzZj1cY5ksgg0y2axz584pXQKpFLNBxjAfJMfs2ahfX/og3+XLQGQkUKeOtBLGpEmAry+wb595H4/02CCTzXJxcVG6BFIpZoOMYT5IjsWyUb68tLrFqVPAjz8Cr7wCPHgAvP02kJxsmce0cWyQyWZ5eHgoXQKpFLNBxjAfJMfi2bCzk5aC27YNePFFaWS5f3/uymcBbJDJZp0+fVrpEkilmA0yhvkgOaWWDQcHYO1aoEIFYMsWYP780nlcG8IGmWxWu3btlC6BVIrZIGOYD5JTqtmoXh1YtUr6fsIEaTtrMhs2yGSz1qxZo3QJpFLMBhnDfJCcUs9G9+5AaKi09Fvv3txYxIy4UYiZcKMQ65OSkgI3NzelyyAVYjbIGOaD5CiSjcxMoFUr4NAh4K23gA0bAJ2udGuwYtwohOgR48aNU7oEUilmg4xhPkiOItlwdATWrZNWuti4EViypPRr0CCOIJsJR5CJiIhIMV99JU2zcHQEDhwAGjdWuiKrYFUjyLm5uZg/fz7q1asHZ2dnVKtWDWPHjsWDBw8ee9/bt29j4cKF6NSpE6pVqwYXFxfUrVsXQ4cOxaVLlwrdfvfu3dDpdAb/vP7665Y4PVKJwMBApUsglWI2yBjmg+Qomo1evYBhw6QpF716AffvK1eLBqhyBHn06NGIjIyEv78/unTpglOnTiEqKgqtW7fGrl27YGcn39f/+OOPeP311/HKK6+gffv28PLywvHjx7Fs2TI4Ojri119/xTPPPKO//e7du9GuXTsMHToUrVu3LnCsp556Cm3btjWpZo4gExERkaLS0oAWLYA//wT69AFiYzkf+TFk+zehMsePHxc6nU4EBAQUuDwyMlIAEF9++aXR+587d06cPXu20OU7d+4UAMRbb71V4PK4uDgBQKxcubJEdTdt2rRE96fS17dvX6VLIJViNsgY5oPkqCIbp04JUbasEIAQ//d/SlejenL9m+qmWKxduxZCCISGhha4fMiQIXB1dUVsbKzR+/v4+KBWrVqFLu/QoQM8PT1x/Phx2fs+ePAA6enpxaqbrM/jskS2i9kgY5gPkqOKbNSr9/CDeiNGACdOKFuPlVJdg3zw4EHY2dmhefPmBS53dnZG48aNcfDgwWId9+7du7h//z6qVKli8PrRo0fDzc0NLi4uqFOnDhYuXAihvtknZEZhYWFKl0AqxWyQMcwHyVFNNoKCpC2o09Kk+cipqUpXZHVU1yBfuXIFXl5ecHJyKnRd1apVkZycjMzMzCIfd8aMGcjKykK/fv0KXO7g4IDu3btjzpw52LRpE5YuXQoPDw+EhoZi4MCBxT4PUr9JkyYpXQKpFLNBxjAfJEdV2Vi0SBpNPnkSGDVK6WqsT6lO9DBBzZo1RbVq1QxeFxQUJACI27dvF+mYGzZsEDqdTnTu3Fnk5uY+9vY5OTmic+fOAoDYu3ev7O2WLVsmmjZtKpo2bSo8PT3Fnj17xPfffy/WrVsnDhw4IBYuXCguXrwoJk6cKLKyskS/fv2EEEIEBgYKIYTo16+fyMrKEhMnThQXL14UCxcuFAcOHBDr1q0T33//vdizZ49YtmyZOHPmjIiIiBD3798Xw4YNE0I8nOeU9zU0NFTcuHFDzJ49W/z5559i1apVYvv27WL79u1i1apV4s8//xSzZ88WN27cEKGhoQaPMWzYMHH//n0REREhzpw5I5YtW6bpc+rTp4/mzkmLz5MS5/TSSy9p7py0+DwpdU55cxa1dE5afJ6UOKdBgwap6pwOfv65yHJwEAIQGwMC+DwZOCe5OciqW8WiUaNGuHHjBq5fv17oul69emHDhg3IyMiAo6OjScfbunUr/P398eyzz+Knn35C+fLlTbrfnj170LZtW4SHh2PmzJmPvT1XsbA+x44dQ6NGjZQug1SI2SBjmA+So8psLF8ODB0KuLkBv/8O1KmjdEWqYjXrIHt7eyM5ORkZGRmFrrt8+TK8vLxMbo5//PFHBAQEoEGDBtixY4fJzTEgfdgPAJKTk02+D1mXw4cPK10CqRSzQcYwHyRHldkYPBh4+20gJUXaSISLEZhEdQ1ys2bNkJubi4SEhAKXp6en4+jRo/Dz8zPpONu3b4e/vz/q1auHXbt2oUKFCkWqIzExEQBkP9RH1u/JJ59UugRSKWaDjGE+SI4qs6HTAcuWAbVqAUePAu+/r3RFVkF1DXLv3r2h0+mwYMGCApcvX74cqamp6Nu3r/6yq1ev4vTp00h95NOZO3bswJtvvok6dergp59+gqenp+zj3bx5s9BlGRkZiIiIAAB069at+CdDREREpLTy5aWtqB0dgc8+AzZuVLoi1bNXuoBHNWrUCCEhIVi0aBECAgLQtWtXnDp1CpGRkWjTpg369Omjv214eDhiYmIQFxen3/Hu0KFDeOONNyCEwIABA7Bt27ZCj5F/K8hXX30V3t7eaNq0Kby9vXHlyhXExsYiMTERI0eOLLTcHGnH1atXlS6BVIrZIGOYD5Kj6mw0aQLMnSutaDFokPR3X1+lq1It1TXIALBgwQL4+PggOjoaW7ZsgZeXF0aOHInp06cb3WYaAI4fP67f7ENuPcL8DXKPHj3w3XffISoqCnfu3EHZsmXx/PPPY9q0aXjnnXfMd1KkOk2aNFG6BFIpZoOMYT5IjuqzMWIE8PPPwHffSfOS9+6VRpWpENWtYmGtuIqF9ZkzZw7Gjx+vdBmkQswGGcN8kByryMbt28DzzwMXLgBjx0qjyjZMrn9jg2wmbJCtT1JSEipVqqR0GaRCzAYZw3yQHKvJxv79QOvWQHY2sHkz8PrrSlekGKtZ5o2otJiyvjXZJmaDjGE+SI7VZKNFCyCv1n79gH//VbYeFeIIsplwBJmIiIisRm6uNHK8bRvw0ktAXBxgr8qPplkUR5CJHpH/w5pE+TEbZAzzQXKsKht2dkBMDODtDezbB/y3vC1JOIJsJhxBJiIiIquzZw/Qvj0gBLB9O9Cxo9IVlSqOIBM9wqre6VOpYjbIGOaD5FhlNtq0kUaPhQACAwE1r+VcijiCbCYcQSYiIiKrlJMDdOokrZHcvj2wYwdQpozSVZUKjiATPSI4OFjpEkilmA0yhvkgOVabjTJlgNhYoHJlqUm2ltU4LIgjyGbCEWTrk5KSAjc3N6XLIBViNsgY5oPkWH02du4EOncGdDqpUW7TpnQeVwhp45I//wQSEwFXV8DTE6hQQfqT9727u9lHtuX6N9tbz4PoP/PmzcPUqVOVLoNUiNkgY5gPkmP12ejYEQgPl0aQ+/QBjh4FzL3xyYMHwPHjUjP8xx/Snz//BO7de/x9dTqpSc7fNBtqpA1d5uYm3d9EHEE2E44gW5/ExETUrl1b6TJIhZgNMob5IDmayEZ2NtC2LfDLL0CXLsAPP0hLwhWVEMDFiw8b4LyviYnSdY+qXBl47jmgXj0gI0PaEvvWrYJf794t/nnZ2xtspP1On+YIMlF+cXFx1v9CRhbBbJAxzAfJ0UQ27O2BtWuBxo2lTUTmzQPGjTN+n9RUaVT40WbYUENrbw/Ury81w88++/DrE088vracHODOnYJNs6FG2tD3qalAUpL0J7+mTQ3/GB5fDZE21atXT+kSSKWYDTKG+SA5mslGtWrAqlVA9+7ApEnSTnstWz4cFc7fBP/xh/yocKVKBRvhvBFiJ6fi1VWmDFCxovSnqPJGpR9toBcuNHhzNshks+7cuaN0CaRSzAYZw3yQHE1lo1s3YMwY4NNPgbfeAmrXlhpiQ+dYpow0Kpy/Ec4bFS7CvF+LcnKS6nl0pJoNMlFBaWlpSpdAKsVskDHMB8nRXDZmzQL27gUOHny4gUjFig+b4LxG+Jlnij8qrFJskMlm+fr6Kl0CqRSzQcYwHyRHc9lwdJQ+pLdhA1CzptQQP/mkekaFLYgbhZDN2r9/v9IlkEoxG2QM80FyNJmNypWBkBBpRQtvb5tojgE2yGTD/P39lS6BVIrZIGOYD5LDbGgHG2SyWYsXL1a6BFIpZoOMYT5IDrOhHdwoxEy4UYj1yc7Ohr09p+FTYcwGGcN8kBxmw/rI9W8cQSabNXjwYKVLIJViNsgY5oPkMBvawRFkM+EIMhEREZF14Qgy0SOCgoKULoFUitkgY5gPksNsaAdHkM2EI8hERERE1oUjyESP6N+/v9IlkEoxG2QM80FymA3t4AiymXAE2frw08Ykh9kgY5gPksNsWB+OIBM9YsqUKUqXQCrFbJAxzAfJYTa0gw0y2azhw4crXQKpFLNBxjAfJIfZ0A42yGSzvv32W6VLIJViNsgY5oPkMBvawQaZbFaLFi2ULoFUitkgY5gPksNsaAcbZLJZ586dU7oEUilmg4xhPkgOs6EdbJDJZrm4uChdAqkUs0HGMB8kh9nQDjbIZLM8PDyULoFUitkgY5gPksNsaAfXQTYTLy8v+Pj4KF0GFUFSUhIqVaqkdBmkQswGGcN8kBxmw/qcP38eycnJhS5ng0w2i5u7kBxmg4xhPkgOs6EdnGJBRERERJQPG2QiIiIionzYIJPNGjp0qNIlkEoxG2QM80FymA3t4BxkIiIiIqJ8OIJMRERERJQPG2QiIiIionzYIBMRERER5cMGmWyKTqcz+MfNzU3p0qiUzJo1Cz179kTNmjWh0+keu8HPX3/9hTfffBMVKlRA2bJl0bp1a/z888+lUyyVqqJkIyIiQvb1ZO7cuaVXNJWKM2fO4MMPP0SLFi1QqVIllCtXDo0bN8bHH3+MBw8eFLo9Xzesn73SBRCVttatWxf6pLGDg4NC1VBpmzRpEjw9PdGkSRPcuXPH6G3//vtvvPjii7C3t8f48ePh7u6O5cuXo3Pnzti2bRs6dOhQOkVTqShKNvLMnz8fXl5eBS5r2rSpBaojJX3++ef47LPP0L17d/Tt2xcODg6Ii4vDBx98gK+++gr79++Hi4sLAL5uaIYgsiEARL9+/ZQugxT0999/679v0KCBqFGjhuxte/bsKezs7MSRI0f0l92/f19Ur15d1KlTR+Tm5lqwUiptRcnG1KlTBQBx7tw5yxdGijt48KC4c+dOocsnT54sAIioqCj9ZXzd0AZOsSCblJmZiZSUFKXLIAXUrFnTpNs9ePAAmzZtQtu2bdG4cWP95W5ubhg8eDDOnDmDgwcPWqhKUoKp2XjUvXv3kJ2dbeZqSE38/Pzg7u5e6PLevXsDAI4fPw6ArxtawgaZbM7XX38NV1dXlCtXDpUrV8bIkSNx9+5dpcsilfnzzz+RkZGBli1bFrquRYsWAMD/6AjPPvss3N3d4ezsjBdffBHbtm1TuiQqRf/++y8AoEqVKgD4uqElnINMNqV58+bo2bMnnn76ady7dw9bt27FokWLsGfPHvz666/8sB7pXblyBQBQtWrVQtflXXb58uVSrYnUw8PDA0OHDsWLL76IChUq4K+//sKCBQvw2muv4fPPP0f//v2VLpEsLCcnB9OnT4e9vT369OkDgK8bWsIGmWzKgQMHCvz93XffxbPPPovJkydj4cKFmDx5skKVkdqkpqYCAJycnApd5+zsXOA2ZHtCQ0MLXTZw4EA0bNgQYWFh6NGjB99wa1xoaCj279+PmTNnom7dugD4uqElnGJBNm/cuHFwdHTEli1blC6FVMTV1RUAkJGRUei69PT0ArchAoCKFSti2LBhuHPnDn799VelyyELmjJlChYtWoShQ4ciPDxcfzlfN7SDDTLZPAcHB3h7eyM5OVnpUkhFvL29ARj+dWjeZYZ+jUq2LW/tZL6eaFdERARmzJiBAQMGYOnSpQWu4+uGdrBBJpuXnp6Of//9V/8hCyIAaNSoEZycnPDbb78Vum7//v0ApE+2E+WXmJgIAHw90ahp06Zh2rRpePfdd7FixQrodLoC1/N1QzvYIJPNuHnzpsHLp0yZguzsbHTr1q2UKyI1c3NzQ7du3bB792788ccf+stTUlKwYsUK1K5dG82bN1ewQlJKdna2wZVvLl26hCVLlqBixYp48cUXFaiMLGn69OmIiIhAUFAQVq5cCTu7wi0UXze0QyeEEEoXQVQawsLCsH//frRr1w7Vq1dHSkoKtm7diri4OLzwwguIi4vT74RE2rV69WpcuHABABAVFYXMzEyMHTsWAFCjRg0EBQXpb3v27Fk0b94cDg4OCAsLQ/ny5bF8+XIcO3YMW7ZsQefOnRU5B7IMU7Nx584d+Pr64s0330T9+vX1q1isWLECKSkpWLt2LXr27KnYeZD5ffbZZxgxYgSqV6+Ojz76qFBzXKVKFXTs2BEAXzc0Q+mdSohKy3fffSc6deokvL29hZOTk3B1dRXPPfec+Pjjj0VaWprS5VEpadOmjQBg8E+bNm0K3f7kyZOie/fuwt3dXbi4uIhWrVqJnTt3ln7hZHGmZiM9PV0MGjRINGzYUHh4eAh7e3vxxBNPiLfeekscOHBAuRMgi+nXr59sNgy9dvB1w/pxBJmIiIiIKB/OQSYiIiIiyocNMhERERFRPmyQiYiIiIjyYYNMRERERJQPG2QiIiIionzYIBMRERER5cMGmYiIiIgoHzbIRERUbG3btoVOp8OqVauULqVIfHx8oNPpsHv3bqVLISIVsle6ACIiInM5f/48Vq1aBQ8PD4SGhipdDhFZKY4gExGRZpw/fx7Tpk3DggULlC6FiKwYG2QiIiIionzYIBMRERER5cMGmYjIiPwf5rp69SqGDRuGatWqwcXFBfXr18f8+fORm5urv/2GDRvQunVreHh4oHz58njttddw/PjxQsfNzMzEli1bMGTIEDz33HPw8vKCs7MzatSogb59++L33383WE94eDh0Oh0qVaqEa9euGbzNq6++Cp1Oh6ZNmyIrK6vEP4Mff/wR7du3h7u7O8qXL48WLVpg9erVJt03MzMTixYtQuvWreHp6QknJyfUqFEDAwcOxKlTpwzep3///tDpdIiIiEB6ejqmTp2KevXqwcXFBZUrV8Y777yDM2fOFLqfj48P2rVrBwC4cOECdDpdgT9yHyS8desWxowZA19fXzg5OaFq1aoYMmQIrl69atoPiIi0RxARkawaNWoIAOLzzz8XTzzxhAAgypcvL8qUKSMACABixIgRQgghJkyYIACIMmXKiHLlyumv9/DwEGfOnClw3M2bN+uvByBcXV2Fs7Oz/u/29vbiiy++KFRPZmamaNKkiQAgunTpUuj6qKgoAUC4uLiIkydPlvj858yZo69Jp9MJDw8PYWdnJwCIMWPGiDZt2ggAYuXKlYXue+XKFfHcc8/p729nZ1fg5+Ls7Cw2btxY6H79+vUTAMTEiRNFixYtBADh6OgoypcvX+DntWfPngL38/PzExUqVNA/VpUqVQr8Wbdunf62ec/r6tWr9d+7uroKJycn/WP4+PiIW7dulfhnSETWhw0yEZERec2Tu7u7aNmypfjjjz+EEEI8ePBAfPTRR/rG8eOPPxYODg5iwYIFIiUlRQghxLFjx0TdunUFANGzZ88Cx42LixMDBgwQP/30k0hOTtZffuHCBREaGqpvIC9cuFCoppMnTwoXFxcBQHz22Wf6y0+fPq2/PDIyssTnvnfvXqHT6QQAERgYKK5evSqEEOL27dti/Pjx+p+LoQY5MzNTNGvWTAAQL7/8soiPjxcZGRlCCCGuXbsmxo4dq29Kz549W+C+eQ2yu7u7cHV1FTExMSIzM1MIIcSRI0f0bxCqVKlSqIGNi4sTAESNGjWMnlve8+rh4SEaN24sfv31VyGEEFlZWeL7778XHh4eAoAYN25ccX98RGTF2CATERmR10hVqFBB3L59u9D17du31484Tps2rdD18fHxAoBwcnLSN4imGDhwoAAgIiIiDF4fGRmpHyk+ffq0yMrKEn5+fgKA6Nixo8jNzTX5seTknVu7du0MHm/QoEH6c3+0QV6+fLkAIJo1aybS09MNHj84OFgAECEhIQUuz2uQAYjY2NhC90tKShIVK1YUAMRHH31U4LqiNshVqlQp8AYlz9y5cwUA4evra/Q4RKRNnINMRGSCYcOGwcPDo9DlHTp0AAA4OjpizJgxha5v1aoVnJ2dkZGRgbNnz5r8eN26dQMA/PLLLwavHzFiBDp37oy0tDQEBgbiww8/xKFDh+Dp6YlVq1ZBp9OZ/FiG3Lp1C3FxcQCACRMmGDzepEmTZO8fExMDAAgJCYGTk5PB2/Tp0wcAsHPnToPX1/j/9u4lJMrvj+P4e8a/WnmZ8jI2qSiBVJSUYRAkmYIY0aLIriAokpGbygypRdnGLAiyC5mLUrFcmEYtNOgq1EZEu2FEC80kvITTlBmNl/kvpGHMGbtMP/v16/MCYTzPOc85jxs/HM7znZgYZx9XYWFh7Nq1C4CrV69O8RTflpubS2ho6KT2DRs2ANDR0cHHjx+9mkNE/jz6ohARke8QHx/vtt1sNgPjL4gFBgZOum40GgkLC6O7uxur1Trh2sDAAOfOnaOxsZEXL15gs9kYHR2d0OfNmzdu5zUYDFy6dIn4+HhaWlpoaWkB4Pz588ybN++Hn+9rbW1tOBwOjEYjSUlJbvvMnz+f6OhoXr9+PaF9ZGSE5uZmAPLz8yksLHQ7/suzfj3+i+TkZI9BPzk5meLiYp49e4bdbsfPz++7nutrK1ascNseGRnp/Pzu3TsCAgJ+6v4i8mdSQBYR+Q4Wi8Vtu4+Pz5TXXfu4VpRob28nNTWV3t5eZ1tQUBAzZ87EYDBgt9uxWq1T7l5aLBaKi4udu6mbN29my5Yt3/9QU+jv7wfAZDJNGQ4jIyMnBdyBgQHsdrvz87d8+vTJ472nmhfGQ7bVaiUiIuKb87gTFBTktn3GjBnOz7+iEoiI/Fl0xEJE5DfIzs6mt7eX5cuXc/PmTT58+MD79+/p7e2lp6eH2tpaABwOh8d7jI6OUlVV5fz90aNH034cwN36XMvePX78GMf4+y5T/vyKeUVEfhUFZBGRadbV1UVzczM+Pj7cuHGD9PT0ScczXHeWPSkpKeHhw4eYTCaio6N5+fIl+/fv/yVrDA8PB8BmszE0NOSxn7tawaGhoc5d8/b29p9eg6fjJa7z+vj4MGfOnJ+eQ0TEHQVkEZFp1t3dDYyHUE/HCG7fvj3lPVpbWzl69CgAZ86cobKyEoPBwIULF2hoaPB6jQkJCRgMBsbGxnjw4IHbPh0dHXR1dU1q9/X1JTExEYD6+vqfXkNTU9M3ry1ZsmTC+WOjcfzfmnaYRcQbCsgiItPMZDIB47vEfX19k64/ffqUK1eueBz/pXLF8PAwGRkZZGZmkpKSwr59+wDIycnh7du3Xq0xJCSE1NRUAE6cOOE2cJaUlHgcn5WVBUBdXZ2zGoYnX7+8+EVnZyc1NTWT2gcGBigvLwfGz127Cg4OBsZ3vkVEfpYCsojINFu0aBFRUVE4HA62bt3qLP82PDxMfX09aWlpbitifFFYWMjz58+xWCyUlZU524uLi1m8eDE9PT3k5uZ6vc6ioiIMBgN37twhKyvLeezDZrNx6NAhysvLnYH0azk5OaxcuZKxsTHWr19PaWnphBf2+vr6qKmpYc2aNZSWlrq9h8lkYufOnVRXVzMyMgLAkydPSE9Pp7+/H7PZTF5e3oQxcXFx+Pr6YrPZqKur8/pvICJ/JwVkEZFpZjQaOX36NEajkfv37xMXF0dwcDCBgYFs2rQJf39/Tp065XbsrVu3OHv2LAAXL16cUMPX39+f6upq/Pz8uHbtGhUVFV6tMykpiePHjwNQVVWFxWIhJCSE0NBQjh07Rn5+PgkJCW7H+vr6cv36dVatWsXQ0BB79+4lLCyMkJAQgoKCiIiIYMeOHTQ1NXks5bZ7927i4+PJzMwkMDAQk8nE0qVLaWlpYdasWdTW1k46fxwQEMD27dsByMjIYPbs2cTGxhIbG+t1zWQR+XsoIIuI/AYbN27k7t27pKWlERQUxPDwMDExMRQUFNDW1kZUVNSkMVarlezsbBwOB3l5eaxdu3ZSn2XLllFUVATAnj176Ozs9GqdBw4coLGxkZSUFAIDAxkZGSExMZGqqipOnjw55Viz2UxTUxOXL19m3bp1mM1mBgcHcTgcLFy4kJycHBoaGjx+4Yi/vz/37t3j8OHDxMTEYLfbCQ8PZ9u2bbS2trJ69Wq348rKyjh48CALFizg8+fPvHr1ilevXjE4OOjV30JE/h4Gh95kEBGRf5GsrCwqKys5cuSIM+yLiEwn7SCLiIiIiLhQQBYRERERcaGALCIiIiLi4n+/ewEiIvLPmjt37g/1LygooKCg4B9ajYjIv58CsojIf9z3fG21q99d7aGiosLrEnUiIt5QQBYR+Y9TsSIRkR+jM8giIiIiIi4UkEVEREREXCggi4iIiIi4UEAWEREREXGhgCwiIiIi4uL/eVcBPZGmD08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 1,\n",
    "                       figsize = (10,7),\n",
    "                       facecolor = 'white');\n",
    "ax.plot(max_depth_range,\n",
    "       r2_list,\n",
    "       lw=2,\n",
    "       color='r')\n",
    "ax.set_xlim([1, max(max_depth_range)])\n",
    "ax.grid(True,\n",
    "       axis = 'both',\n",
    "       zorder = 0,\n",
    "       linestyle = ':',\n",
    "       color = 'k')\n",
    "ax.tick_params(labelsize = 18)\n",
    "ax.set_xlabel('max_depth', fontsize = 24)\n",
    "ax.set_ylabel('R^2', fontsize = 24)\n",
    "ax.set_title('Model Performance on Test Set', fontsize = 24)\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('images/Model_Performance.png', dpi = 300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of values to try for max_depth:\n",
    "max_depth_range = list(range(1, 25))\n",
    "\n",
    "# List to store the average RMSE for each value of max_depth:\n",
    "r2_test_list = []\n",
    "\n",
    "r2_train_list = []\n",
    "\n",
    "for depth in max_depth_range:\n",
    "    \n",
    "    reg = DecisionTreeRegressor(max_depth = depth, \n",
    "                             random_state = 0)\n",
    "    reg.fit(X_train, y_train)    \n",
    "    \n",
    "    score = reg.score(X_test, y_test)\n",
    "    r2_test_list.append(score)\n",
    "    \n",
    "    # Bad practice: train and test the model on the same data\n",
    "    score = reg.score(X_train, y_train)\n",
    "    r2_train_list.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph below shows that the best R2 for the model is when the parameter max_depth is equal to 5. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://matplotlib.org/3.5.0/tutorials/text/annotations.html\n",
    "\n",
    "https://jakevdp.github.io/PythonDataScienceHandbook/04.09-text-and-annotation.html\n",
    "\n",
    "Remember how to Create beautiful Legends outside plottin area: https://www.linkedin.com/learning/python-for-data-visualization/basics-of-matplotlib?autoplay=true\n",
    "\n",
    "Can say optimal model complexity instead: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff#/media/File:Bias_and_variance_contributing_to_total_error.svg\n",
    "\n",
    "Use this graph as a base: https://jakevdp.github.io/PythonDataScienceHandbook/06.00-figure-code.html#Validation-Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.0, 24.0)\n",
      "(0.2, 1.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (10,7), facecolor = 'white');\n",
    "\n",
    "ax.plot(max_depth_range,\n",
    "        r2_train_list,\n",
    "        lw=2,\n",
    "        color='b',\n",
    "        label = 'Training')\n",
    "\n",
    "ax.plot(max_depth_range,\n",
    "        r2_test_list,\n",
    "        lw=2,\n",
    "        color='r',\n",
    "        label = 'Test')\n",
    "\n",
    "ax.set_xlim([1, max(max_depth_range)])\n",
    "ax.grid(True,\n",
    "        axis = 'both',\n",
    "        zorder = 0,\n",
    "        linestyle = ':',\n",
    "        color = 'k')\n",
    "ax.tick_params(labelsize = 18)\n",
    "ax.set_xlabel('max_depth', fontsize = 24)\n",
    "ax.set_ylabel('R^2', fontsize = 24)\n",
    "ax.set_ylim(.2,1)\n",
    "\n",
    "ax.legend(loc = 'center right', fontsize = 20, framealpha = 1)\n",
    "\n",
    "ax.annotate(\"Best Model\",\n",
    "            xy=(5, 0.5558073822490773), xycoords='data',\n",
    "            xytext=(5, 0.4), textcoords='data', size = 20,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc3\",\n",
    "                            color  = 'black', \n",
    "                            lw =  2),\n",
    "            ha = 'center',\n",
    "            va = 'center',\n",
    "            bbox={'facecolor':'white', 'edgecolor':'none', 'pad':5}\n",
    "            )\n",
    "\n",
    "\n",
    "ax.set_title('Model Performance on Training vs Test Set', fontsize = 24)\n",
    "\n",
    "# Annotating by figure fraction for ease because i want it outside the plotting area. \n",
    "ax.annotate('High Bias',\n",
    "            xy=(.1, .032), xycoords='figure fraction', size = 12)\n",
    "\n",
    "ax.annotate('High Variance',\n",
    "            xy=(.82, .032), xycoords='figure fraction', size = 12)\n",
    "\n",
    "# xy=(-3.01,0.015), xytext=(-3.41,0.20)\n",
    "temp = ax.get_xlim()\n",
    "print(temp)\n",
    "temp1 = ax.get_ylim()\n",
    "print(temp1)\n",
    "fig.tight_layout()\n",
    "fig.savefig('images/max_depth_vs_R2_Best_Model.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption for the image: It is important to keep in mind that max_depth is not the same thing as depth of a decision tree. max_depth is a way to preprune a decision tree. In other words, if a tree is already as pure as possible at a depth, it will not continue to split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this tutorial went over how to tune using max_depth, keep in mind that there are other things to hyperparameter tune like selection criterion (\"mse\", \"friedman_mse\", \"mae\"), minimum samples for a node to split (min_samples_lead), max number of leaf nodes (max_leaf_nodes), and more. If you want to learn about the terms in this section, I highly encourage you to check out my [Understanding Decision Trees for Classification (Python) tutorial](https://towardsdatascience.com/understanding-decision-trees-for-classification-python-9663d683c952). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Conclusion </h2>\n",
    "\n",
    "A goal of supervised learning is to build a model that performs well on new data which train test split helps you simulate. With any model validation procedure it is important to keep in mind some advantages and disadvantages. Some advantages of this procedure is that it is relatively simple and that it can help avoid overly complex models don't generalize well to new data. Some disadvantages of the procedure is that it eliminates data that could otherwise be used for training a machine learning model (your testing data isn't used for training) and that for more models cases you may wish to consider using a training, validation, and test set. Future tutorials will cover other model validation procedures like cross validation which help mitigate these issues. If you have any questions or thoughts on the tutorial, feel free to reach out in the comments below or through <a href=\"https://twitter.com/GalarnykMichael\">Twitter</a>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(range(1, 25)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(max_depth_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
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}
